Intelligent information processing using neural networks and genetic algorithms

Abstract

Intelligent information processing (IIP) or the smart processing of signals in communication systems and data measurements from multi-sensor systems are needed for advanced microautonomous applications. A balanced combination of efficient algorithms, fast networks, and collaboration of the different technologies are required for smaller, faster, and more efficient system-on-a-chip applications. In this paper we present guidelines/approach for intelligent information processing using neural networks (NNs) and genetic algorithms (GAs) which are capable of learning through discovery and/or reinforcement with features optimization through chromosome mutations of GAs. Specific details about a special application for electronic-nose (EN) implementation to discriminate among four chemicals, using reinforcement NN implemented tiny-chip and a GA system implementation is presented with test results.