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Associative Hopfield Memory is a very useful form of Artificial Neural Networks. This paper outlinesan optimization relaxation approach for signature verification based on the Hopfield neural network (HNN). The standard sample signature of the customer is cross matched with the one supplied on the check. The difference percentage is obtained by calculating the different pixels in both the images. The network topology is built so that each pixel in the difference image is a neuron in the network. Each neuron is categorized by its status, which in turn signifies that if the particular pixel is changed. The network converges to unwavering condition based on theenergy function which is derived in experiments. The Hopfield‟s model allows each node to take on two binary state values (changed/unchanged) for each pixel. The performance of the proposed technique is evaluated by applying it in various binary and gray scale images. This paper contributes to finding an automated scheme for verification of authentic signature on bank check. The derived energy function allows a trade-off between the influence of its neighborhood and its own criterion. This device is able to recall as well as complete partially specified inputs. The network is trained via a storage prescription that forces stable states to correspond to (local)minima of a network "energy" function. [ABSTRACT FROM AUTHOR]

Various methods have been proposed for the automatic speech recognition system. The main emphasis has been given to the signal or voice classification of individuals. In this paper, we are considering the four prominent neural network architectures namely, Multilayer Feed-Forward Neural Network, Cascade Backpropagation Network, Elman Backpropagation Network and Adaptive Linear Neural Network for the performance evaluation on collected sound samples of ten different people. These networks have been trained using mini-batch stochastic gradient descent learning with batch normalization. The feed-forward neural network model has been extended up to the four hidden layers to perform the desired classification of sound samples. The collected samples of the analog form of sound signals are converted into the digital form using the digital signal processing technique. The time & frequency patches of these digital signals have been considered as the training pattern samples. The test pattern sets have also been constructed in the same manner and the performances of these neural network architectures have been measured. The simulation results are exhibiting the better performance of Cascade Neural Network over the other architectures. [ABSTRACT FROM AUTHOR]