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]
- Water Quality Modeling in Reservoirs Using Multivariate Linear Regression
and Two Neural Network Models
Abstract: In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient, computed from the measured and the model-simulated DO, TP, Chl a, and SD values. The results indicate that the performance of the ANFIS model is superior to those of the MLR and RBFN models. The study results show that the neural network using the ANFIS model is suitable for simulating the water quality variables with reasonable accuracy, suggesting that the ANFIS model can be used as a valuable tool for reservoir management in Taiwan.
PubDate: Tue, 09 Jun 2015 06:15:00 +000
- 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