A new learning algorithm for feed-forward neural networks based on linear programming is introduced. This alternative to back-propagation gives faster and more reliable learning on reasonably sized examples. Extensions of the method for efficient (approximate) implementations in large networks are considered.

In this paper is presented an investigation of the speech recognition classification performance. This investigation on the speech recognition classification performance is performed using two standard neural networks structures as the classifier. The utilized standard neural network types include Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions Neur...

Handwritten character recognition plays an important role in the modern world. It can solve more complex problems and makes human’s job easier. The present paper portrays a novel approach in recognizing handwritten devanagari character through feed forward back propagation neural network. All the experiments are conducted by using the Artificial Neural Network tool of Matlab.

We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, an...

In this paper, we proposed the design method of artificial neural networks using VHDL and implement in FPGA. VHDL is a programming language that has been designed and optimized for describing the behavior of digital systems. Back propagation algorithm for the design of a neuron is described. Back propagation is popular training algorithms for multilayer perceptrons. Over the last years many imp...