ARTIFICIAL NEURAL NETWORKS
Objectives - History- Biological Inspiration- Neuron Model- Single- Input Neuron-Multi-Input Neuron- Network Architectures- A Layer of Neurons-Multiple Layers of Neurons.
Perceptron Architecture- Single-Neuron Perceptron- Multi-Neuron Perceptron- Perceptron Learning Rule- Constructing Learning Rules- Training Multiple-Neuron Perceptrons.
Simple Associative Networks- Unsupervised Hebb Rule- Hebb Rule with Decay-Instar Rule-Outstar Rule- Kohonen Rule.
Adaline Network- Madaline Network -Mean Square Error- LMS Algorithm- Back Propagationa Neural networks – Hopfield Networks
Adaptive Filtering- Adaptive Noise Cancellation- Forecasting – Neural control applications – Character recognition.
Hagan Demuth Beale, ‘Neural network design’, PWS publishing company, 1995
Freeman, J.A and Skapura, D.M., ‘Neural networks-Algorithms, applications and programming techniques’ Addison Wesley, 1991
Satish Kumar,’ ‘ Neural Networks – A classroom approach’, Tata McGraw-Hill Publishing Company Limited, 2004
