Lecture 13: Introduction to Neural Networks

27-642/sde 733 Artiicial Neural Networks Lecture Notes Introductory Material Introduction to Classical Ai

Lecture 1: Introduction

features. In the last 5 years or so, very deep networks have indeed been found to achieve startlingly good performance on a wide variety of problems in vision and other application areas; for this reason, the research area of neural networks is often referred to as deep learning. There is some circumstantial evidence that deep networks learn hierarchical representations, but this is notoriously...

INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

Robust stability of stochastic fuzzy impulsive recurrent neural networks with\ time-varying delays

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

Artificial neural networks in high-energy physics

Artificial neural networks are the machine learning technique best known in the high-energy physics community. Introduced in the field in 1988, followed by a decade of tests and applications received with reticence by the community, they became a common tool in high-energy physics data analysis. Important physics results have been extracted using this method in the last decade. This lecture mak...