aritficial neural networks
Semester : VI
Course Code : 18EC642
CIE Marks : 40 SEE Marks : 60
ARTIFICIAL NEURAL NETWORKS
Introduction: Biological Neuron — Artiﬁcial Neural Model – Types of activation functions — Architecture: Feedforward and Feedback, Convex Sets, Convex Hull and Linear Separability, Non-Linear Separable Problem. XOR Problem, Multilayer Networks.
Learning: Learning Algorithms, Error correction and Gradient Descent Rule Learning objective of TLNs, Perceptron Learning Algorithm, Perceptron Convergence Theorem.
Supervised Learning: Perceptron learning and Non Separable sets, (x-Least Mean Square Learning, MSE Error surface, Steepest Descent Search, u-LMS approximate to gradient descent, Application of LMS to Noise Cancelling, Multi-layered Network Architecture, Backpropagation Learning Algorithm, Practical consideration of BP algorithm.
Support Vector Machines and Radial Basis Function: Learning from examples, Statistical Learning Theory, Support Vector Machines, SVM application to Image Classiﬁcation, Radial Basis Function Regularization theory, Generalized RBF Networks, Learning in RBFNs, RBF application to face recognition.
Attractor Neural Networks: Associative Learning Attractor AssociativeMemory, Linear Associative memory, Hopﬁeld Network, application of HopﬁeldNetwork, Brain State in a Box neural Network, Simulated Annealing, BoltzmannMachine, Bidirectional Associative Memory.
Self-organization Feature Map: Maximal Eigenvector Filtering, ExtractingPrincipal Components, Generalized Learning Laws, Vector Quantization, Self-organization Feature Maps, Application of SOM, Growing Neural Gas.
- digital communication
- embedded systems
- microwave and antennas
- operating system
- artificial neural networks
- data structures using C++
- digital system design using verilog
- nano electronics
- python application programming
- signal processing
- sensors and signal conditioning
- virtual instrumentation
- basic VLSI Design