This study investigates the effectiveness of a genetic algorithm (GA) evolved neural network (NN) classifier and its application to the classification of premature ventricular contraction (PVC) beats. As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we ...

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. Howeve...

Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. The hybrid system gives better recognition result due to better discrimination capability of the NN. A major problem in handwriting recognition is the huge variability and distortions of patterns. Elastic models based on local observations and dynamic programm...

Neural Networks (NN) have proved its efficacy for nonlinear system modeling. NN based controllers and estimators for nonlinear systems provide promising alternatives to the conventional counterpart. However, NN models have to meet the stringent requirements on execution time for its effective use in real time applications. This requires the NN model to be structurally compact and computationall...

In this paper, adaptive neural network sliding-mode controller design approach with decoupled method is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear system. The adaptive neural sliding mode control system is comprised of neural network (NN) and a compensation controller. The NN is the main tracking controller, which i...

The field of neurocontrol, in which neural networks are used for control of complex systems, has many potential applications. One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks ...

The side-match finite-state vector quantization (SMVQ) schemes improve performance over the vector quantization by exploiting the neighboring vector correlations within the image. In this paper, we propose a neural network side-match finite-state vector quantization (NN-SMVQ) scheme that combines the techniques of neural network prediction and the SMVQ coding method. In our coding scheme, the m...

The identification of non-linear systems by artificial neural networks has been successfully applied in many applications. In this context, the radial basis function neural network (RBF-NN) is a powerful approach for non-linear system identification. An RBF neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden layer contain Gaussian transfer functions ...

In this paper, the use of a proposed recurrent neural network control system to control a four-legged walking robot is investigated. The control system consists of a neural controller, standard PD controller and the walking robot. The robot is a planar four-legged walking robot. The proposed Neural Network (NN) is employed as an inverse controller of the robot. The NN has three layers, which ar...

a critical point is a point at which the derivatives of an error function are all zero. It has been shown in the literature that critical points caused by the hierarchical structure of a realvalued neural network (NN) can be local minima or saddle points, although most critical points caused by the hierarchical structure are saddle points in the case of complex-valued neural networks. Several s...