Advances in Artificial Neural Systems
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Open Access journal
ISSN (Print) 1687-7594 - ISSN (Online) 1687-7608
Published by Hindawi Publishing Corporation [403 journals]
- Application of Neural Network Modeling to Identify Auditory Processing
Disorders in School-Age Children
Abstract: P300 Auditory Event-Related Potentials (P3AERPs) were recorded in nine school-age children with auditory processing disorders and nine age- and gender-matched controls in response to tone burst stimuli presented at varying rates (1/second or 3/second) under varying levels of competing noise (0 dB, 40 dB, or 60 dB SPL). Neural network modeling results indicated that speed of information processing and task-related demands significantly influenced P3AERP latency in children with auditory processing disorders. Competing noise and rapid stimulus rates influenced P3AERP amplitude in both groups.
PubDate: Thu, 30 Apr 2015 13:17:06 +000
- Generalisation over Details: The Unsuitability of Supervised
Backpropagation Networks for Tetris
Abstract: We demonstrate the unsuitability of Artificial Neural Networks (ANNs) to the game of Tetris and show that their great strength, namely, their ability of generalization, is the ultimate cause. This work describes a variety of attempts at applying the Supervised Learning approach to Tetris and demonstrates that these approaches (resoundedly) fail to reach the level of performance of hand-crafted Tetris solving algorithms. We examine the reasons behind this failure and also demonstrate some interesting auxiliary results. We show that training a separate network for each Tetris piece tends to outperform the training of a single network for all pieces; training with randomly generated rows tends to increase the performance of the networks; networks trained on smaller board widths and then extended to play on bigger boards failed to show any evidence of learning, and we demonstrate that ANNs trained via Supervised Learning are ultimately ill-suited to Tetris.
PubDate: Mon, 27 Apr 2015 09:08:34 +000
- Sensorless Direct Power Control of Induction Motor Drive Using Artificial
Abstract: This paper proposes the design of sensorless induction motor drive based on direct power control (DPC) technique. It is shown that DPC technique enjoys all advantages of pervious methods such as fast dynamic and ease of implementation, without having their problems. To reduce the cost of drive and enhance the reliability, an effective sensorless strategy based on artificial neural network (ANN) is developed to estimate rotor’s position and speed of induction motor. Developed sensorless scheme is a new model reference adaptive system (MRAS) speed observer for direct power control induction motor drives. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Simulink. Some simulations are carried out for the closed-loop speed control systems under various load conditions to verify the proposed methods. Simulation results confirm the performance of ANN based sensorless DPC induction motor drive in various conditions.
PubDate: Mon, 30 Mar 2015 09:14:24 +000
- Stochastic Search Algorithms for Identification, Optimization, and
Training of Artificial Neural Networks
Abstract: This paper presents certain stochastic search algorithms (SSA) suitable for effective identification, optimization, and training of artificial neural networks (ANN). The modified algorithm of nonlinear stochastic search (MN-SDS) has been introduced by the author. Its basic objectives are to improve convergence property of the source defined nonlinear stochastic search (N-SDS) method as per Professor Rastrigin. Having in mind vast range of possible algorithms and procedures a so-called method of stochastic direct search (SDS) has been practiced (in the literature is called stochastic local search-SLS). The MN-SDS convergence property is rather advancing over N-SDS; namely it has even better convergence over range of gradient procedures of optimization. The SDS, that is, SLS, has not been practiced enough in the process of identification, optimization, and training of ANN. Their efficiency in some cases of pure nonlinear systems makes them suitable for optimization and training of ANN. The presented examples illustrate only partially operatively end efficiency of SDS, that is, MN-SDS. For comparative method backpropagation error (BPE) method was used.
PubDate: Sat, 28 Feb 2015 07:30:27 +000
- Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector
Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes
Abstract: There is a necessity for analysis of a large amount of data in many fields such as healthcare, business, industries, and agriculture. Therefore, the need of the feature selection (FS) technique for the researchers is quite evident in many fields of science, especially in computer science. Furthermore, an effective FS technique that is best suited to a particular learning algorithm is of great help for the researchers. Hence, this paper proposes a hybrid feature selection (HFS) based efficient disease diagnostic model for Breast Cancer, Hepatitis, and Diabetes. A HFS is an efficient method that combines the positive aspects of both Filter and Wrapper FS approaches. The proposed model adopts weighted least squares twin support vector machine (WLSTSVM) as a classification approach, sequential forward selection (SFS) as a search strategy, and correlation feature selection (CFS) to evaluate the importance of each feature. This model not only selects relevant feature subset but also efficiently deals with the data imbalance problem. The effectiveness of the HFS based WLSTSVM approach is examined on three well-known disease datasets taken from UCI repository with the help of predictive accuracy, sensitivity, specificity, and geometric mean. The experiment confirms that our proposed HFS based WLSTSVM disease diagnostic model can result in positive outcomes.
PubDate: Wed, 21 Jan 2015 14:30:32 +000
- Neural Virtual Sensors for Adaptive Magnetic Localization of Autonomous
Abstract: The surging advance in micro- and nanotechnologies allied with neural learning systems allows the realization of miniaturized yet extremely powerful multisensor systems and networks for wide application fields, for example, in measurement, instrumentation, automation, and smart environments. Time and location context is particularly relevant to sensor swarms applied for distributed measurement in industrial environment, such as, for example, fermentation tanks. Common RF solutions face limits here, which can be overcome by magnetic systems. Previously, we have developed the electronic system for an integrated data logger swarm with magnetic localization and sensor node timebase synchronization. The focus of this work is on an approach to improving both localization accuracy and flexibility by the application of artificial neural networks applied as virtual sensors and classifiers in a hybrid dedicated learning system. Including also data from an industrial brewery environment, the best investigated neural virtual sensor approach has achieved an advance in localization accuracy of a factor of 4 compared to state-of-the-art numerical methods and, thus, results in the order of less than 5 cm meeting industrial expectations on a feasible solution for the presented integrated localization system solution.
PubDate: Tue, 30 Dec 2014 08:15:28 +000
- An Overview of Transmission Line Protection by Artificial Neural Network:
Fault Detection, Fault Classification, Fault Location, and Fault Direction
Abstract: Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.
PubDate: Sun, 28 Dec 2014 09:22:34 +000
- Optimal Design of PID Controller for the Speed Control of DC Motor by
Using Metaheuristic Techniques
Abstract: DC motors are used in numerous industrial applications like servo systems and speed control applications. For such systems, the Proportional+Integral+Derivative (PID) controller is usually the controller of choice due to its ease of implementation, ruggedness, and easy tuning. All the classical methods for PID controller design and tuning provide initial workable values for , , and which are further manually fine-tuned for achieving desired performance. The manual fine tuning of the PID controller parameters is an arduous job which demands expertise and comprehensive knowledge of the domain. In this research work, some metaheuristic algorithms are explored for designing PID controller and a comprehensive comparison is made between these algorithms and classical techniques as well for the purpose of selecting the best technique for PID controller design and parameters tuning.
PubDate: Wed, 10 Dec 2014 00:10:05 +000
- Architecture Analysis of an FPGA-Based Hopfield Neural Network
Abstract: Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA) hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.
PubDate: Tue, 09 Dec 2014 00:10:05 +000
- ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance
Abstract: This paper proposes a novel neural network architecture based on adaptive resonance theory (ART) called ARTgrid that can perform both online and offline clustering of 2D object structures. The main novelty of the proposed architecture is a two-level categorization and search mechanism that can enhance computation speed while maintaining high performance in cases of higher vigilance values. ARTgrid is developed for specific robotic applications for work in unstructured environments with diverse work objects. For that reason simulations are conducted on random generated data which represents actual manipulation objects, that is, their respective 2D structures. ARTgrid verification is done through comparison in clustering speed with the fuzzy ART algorithm and Adaptive Fuzzy Shadow (AFS) network. Simulation results show that by applying higher vigilance values () clustering performance of ARTgrid is considerably better, while lower vigilance values produce comparable results with the original fuzzy ART algorithm.
PubDate: Wed, 03 Dec 2014 07:28:57 +000
- An Electronic Circuit Model of the Interpostsynaptic Functional LINK
Designed to Study the Formation of Internal Sensations in the Nervous
Abstract: The nervous system makes changes in response to the continuous arrival of associative learning stimuli from the environment and executes behavioral motor activities after making predictions based on past experience. The system exhibits dynamic plasticity changes that involve the formation of the first-person internal sensations of perception, memory, and consciousness to which only the owner of the nervous system has access. These properties of natural intelligence need to be verified for their mechanism of formation using engineered systems so that a third person can access them. In the presence of a synaptic junctional delay of up to two milliseconds, we anticipate that the systems property of formation of internal sensations is likely independent of the mode of conduction along the neuronal processes. This allows testing for the formation of internal sensations using electronic circuits. The present work describes the neurobiological context for the formation of the basic units of inner sensations that occur through the reactivation of interpostsynaptic functional LINKs and its connection to motor activity. These mechanisms are translated to an analogue circuit unit for the development of robotic systems.
PubDate: Wed, 03 Dec 2014 06:48:37 +000
- Virtual Sensor for Calibration of Thermal Models of Machine Tools
Abstract: Machine tools are important parts of high-complex industrial manufacturing. Thus, the end product quality strictly depends on the accuracy of these machines, but they are prone to deformation caused by their own heat. The deformation needs to be compensated in order to assure accurate production. So an adequate model of the high-dimensional thermal deformation process must be created and parameters of this model must be evaluated. Unfortunately, such parameters are often unknown and cannot be calculated a priori. Parameter identification during real experiments is not an option for these models because of its high engineering and machine time effort. The installation of additional sensors to measure these parameters directly is uneconomical. Instead, an effective calibration of thermal models can be reached by combining real and virtual measurements on a machine tool during its real operation, without additional sensors installation. In this paper, a new approach for thermal model calibration is presented. The expected results are very promising and can be recommended as an effective solution for this class of problems.
PubDate: Thu, 27 Nov 2014 14:03:15 +000
- Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial
Abstract: Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA) stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.
PubDate: Tue, 11 Nov 2014 06:03:21 +000
- Global Stability, Bifurcation, and Chaos Control in a Delayed Neural
Abstract: Conditions for the global asymptotic stability of delayed artificial neural network model of n (≥3) neurons have been derived. For bifurcation analysis with respect to delay we have considered the model with three neurons and used suitable transformation on multiple time delays to reduce it to a system with single delay. Bifurcation analysis is discussed with respect to single delay. Numerical simulations are presented to verify the analytical results. Using numerical simulation, the role of delay and neuronal gain parameter in changing the dynamics of the neural network model has been discussed.
PubDate: Wed, 08 Oct 2014 06:51:42 +000
- Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and
Teaching Learning Based Optimization Algorithm
Abstract: Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using -fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.
PubDate: Wed, 17 Sep 2014 00:00:00 +000
- Long Time Behavior for a System of Differential Equations with
Abstract: We consider a general system of nonlinear ordinary differential equations of first order. The nonlinearities involve distributed delays in addition to the states. In turn, the distributed delays involve nonlinear functions of the different variables and states. An explicit bound for solutions is obtained under some rather reasonable conditions. Several special cases of this system may be found in neural network theory. As a direct application of our result it is shown how to obtain global existence and, more importantly, convergence to zero at an exponential rate in a certain norm. All these nonlinearities (including the activation functions) may be non-Lipschitz and unbounded.
PubDate: Sun, 14 Sep 2014 00:00:00 +000
- A Hybrid Intelligent Method of Predicting Stock Returns
Abstract: This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of both a linear and a nonlinear model. The model has been tested on stock data obtained from National Stock Exchange of India. The results indicate that the proposed model can be a promising approach in predicting future stock movements.
PubDate: Sun, 07 Sep 2014 08:26:42 +000
- Downscaling Statistical Model Techniques for Climate Change Analysis
Applied to the Amazon Region
Abstract: The Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the integrity of the global climate: tropical forests and woodlands (e.g., savannas) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climates. Consider the importance of the Amazon biome to the global climate changes impacts and the role of the protected area in the conservation of biodiversity and state-of-art of downscaling model techniques based on ANN Calibrate and run a downscaling model technique based on the Artificial Neural Network (ANN) that is applied to the Amazon region in order to obtain regional and local climate predicted data (e.g., precipitation). Considering the importance of the Amazon biome to the global climate changes impacts and the state-of-art of downscaling techniques for climate models, the shower of this work is presented as follows: the use of ANNs good similarity with the observation in the cities of Belém and Manaus, with correlations of approximately 88.9% and 91.3%, respectively, and spatial distribution, especially in the correction process, representing a good fit.
PubDate: Thu, 29 May 2014 13:26:24 +000
- Exponential Stability of Periodic Solution to Wilson-Cowan Networks with
Time-Varying Delays on Time Scales
Abstract: We present stability analysis of delayed Wilson-Cowan networks on time scales. By applying the theory of calculus on time scales, the contraction mapping principle, and Lyapunov functional, new sufficient conditions are obtained to ensure the existence and exponential stability of periodic solution to the considered system. The obtained results are general and can be applied to discrete-time or continuous-time Wilson-Cowan networks.
PubDate: Wed, 02 Apr 2014 11:56:19 +000
- Oscillatory Behavior on a Three-Node Neural Network Model with Discrete
and Distributed Delays
Abstract: This paper investigates the oscillatory behavior of the solutions for a three-node neural network with discrete and distributed delays. Two theorems are provided to determine the conditions for oscillating solutions of the model. The criteria for selecting the parameters in this network are derived. Some simulation examples are presented to illustrate the effectiveness of the results.
PubDate: Mon, 24 Feb 2014 13:56:51 +000
- Novel Discrete Compactness-Based Training for Vector Quantization
Networks: Enhancing Automatic Brain Tissue Classification
Abstract: An approach for nonsupervised segmentation of Computed Tomography (CT) brain slices which is based on the use of Vector Quantization Networks (VQNs) is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC) which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric. Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.
PubDate: Mon, 30 Dec 2013 08:53:57 +000
- Estimation of Static Pull-In Instability Voltage of Geometrically
Nonlinear Euler-Bernoulli Microbeam Based on Modified Couple Stress Theory
by Artificial Neural Network Model
Abstract: In this study, the static pull-in instability of beam-type micro-electromechanical system (MEMS) is theoretically investigated. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. Two supervised neural networks, namely, back propagation (BP) and radial basis function (RBF), have been used for modeling the static pull-in instability of microcantilever beam. These networks have four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data employed for training the networks and capabilities of the models in predicting the pull-in instability behavior has been verified. Based on verification errors, it is shown that the radial basis function of neural network is superior in this particular case and has the average errors of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations show a good agreement, which also proves the feasibility and effectiveness of the adopted approach.
PubDate: Thu, 26 Dec 2013 13:29:13 +000
- Comparison of Artificial Neural Network Architecture in Solving Ordinary
Abstract: This paper investigates the solution of Ordinary Differential Equations (ODEs) with initial conditions using Regression Based Algorithm (RBA) and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases). Initial weights are taken as combination of random as well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.
PubDate: Sun, 15 Dec 2013 14:56:15 +000
- Artificial Neural Network Modeling for Biological Removal of Organic
Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch
Abstract: The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and -N level of 2000 ± 100 mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models “A,” “B,” and “C”) using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models “A,” “B,” and “C”) were trained and tested reasonably well to predict COD and -N removal efficiently with 3.33% experimental error.
PubDate: Thu, 12 Dec 2013 07:54:34 +000
- The Classification of Valid and Invalid Beats of Three-Dimensional
Nystagmus Eye Movement Signals Using Machine Learning Methods
Abstract: Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering. We measured the spontaneous nystagmus of 107 otoneurological patients to form a training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts. Video-oculography was used to record three-dimensional nystagmus signals. Firstly, a procedure was implemented to accept or reject nystagmus beats according to the limits for nystagmus variables. Secondly, an expert perused all nystagmus beats manually. Thirdly, both the machine and the manual results were united to form the third variation of the training set for the machine learning-based classification. This improved accuracy results in classification; high accuracy values of up to 89% were obtained.
PubDate: Tue, 10 Dec 2013 08:13:00 +000
- Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A
Low Cost Alternative to Experimentation
Abstract: Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs) are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.
PubDate: Wed, 23 Oct 2013 08:55:06 +000
- Desirability Improvement of Committee Machine to Solve Multiple Response
Abstract: Multiple response optimization (MRO) problems are usually solved in three phases that include experiment design, modeling, and optimization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) is used for modeling phase. Also, the optimization phase is done with different optimization techniques such as genetic algorithm (GA). The current paper is a development of recent authors' work on application of CM in MRO problem solving. In the modeling phase, the CM weights are determined with GA in which its fitness function is minimizing the RMSE. Then, in the optimization phase, the GA specifies the final response with the object to maximize the global desirability. Due to the fact that GA has a stochastic nature, it usually finds the response points near to optimum. Therefore, the performance the algorithm for several times will yield different responses with different GD values. This study includes a committee machine with four different ANNs. The algorithm was implemented on five case studies and the results represent for selected cases, when number of performances is equal to five, increasing in maximum GD with respect to average value of GD will be eleven percent. Increasing repeat number from five to forty-five will raise the maximum GD by only about three percent more. Consequently, the economic run number of the algorithm is five.
PubDate: Mon, 16 Sep 2013 10:27:50 +000
- Variance Sensitivity Analysis of Parameters for Pruning of a Multilayer
Perceptron: Application to a Sawmill Supply Chain Simulation Model
Abstract: Simulation is a useful tool for the evaluation of a Master Production/Distribution Schedule (MPS). The goal of this paper is to propose a new approach to designing a simulation model by reducing its complexity. According to the theory of constraints, a reduced model is built using bottlenecks and a neural network exclusively. This paper focuses on one step of the network model design: determining the structure of the network. This task may be performed by using the constructive or pruning approaches. The main contribution of this paper is twofold; it first proposes a new pruning algorithm based on an analysis of the variance of the sensitivity of all parameters of the network and then uses this algorithm to reduce the simulation model of a sawmill supply chain. In the first step, the proposed pruning algorithm is tested with two simulation examples and compared with three classical pruning algorithms from the literature. In the second step, these four algorithms are used to determine the optimal structure of the network used for the complexity-reduction design procedure of the simulation model of a sawmill supply chain.
PubDate: Sat, 14 Sep 2013 10:39:38 +000
- Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent
Abstract: This paper presents a neural scheme for controlling an actuator of pneumatic control valve system. Bondgraph method has been used to model the actuator of control valve, in order to compare the response characteristics of valve. The proposed controller is such that the system is always operating in a closed loop, which should lead to better performance characteristics. For comparison, minimum- and full-order observer controllers are also utilized to control the actuator of pneumatic control valve. Simulation results give superior performance of the proposed neural control scheme.
PubDate: Sun, 23 Jun 2013 08:48:25 +000
- Visualizing Clusters in Artificial Neural Networks Using Morse Theory
Abstract: This paper develops a process whereby a high-dimensional clustering problem is solved using a neural network and a low-dimensional cluster diagram of the results is produced using the Mapper method from topological data analysis. The low-dimensional cluster diagram makes the neural network's solution to the high-dimensional clustering problem easy to visualize, interpret, and understand. As a case study, a clustering problem from a diabetes study is solved using a neural network. The clusters in this neural network are visualized using the Mapper method during several stages of the iterative process used to construct the neural network. The neural network and Mapper clustering diagram results for the diabetes study are validated by comparison to principal component analysis.
PubDate: Thu, 20 Jun 2013 13:26:11 +000