Authors: H.S. Abdel-Aty-Zohdy and R.L. Ewing
Affilation: Oakland University, United States
Pages: 361 - 364
Keywords: bio-Inspired, neural networks, genetic algorithms, plastic NNs, electronic nose
Bio-inspired systems utilizing neural networks and genetic algorithms are presented in this paper for pattern discovery, identification, classification, and optimum choices for Signal Perception and Processing (SPP). Sample applications are presented for: (I) Integrated Intelligent E-Nose Systems, and (II) Communication Systems dealing with: (1) Connection admission control, (2) Network congestion, (3) Resource management, (4) Priorities and constrains of service control, and (5) Application-specific network adaptation. The presented bio-inspired systems include: i-Self-Organizing feature Map for discovery, ii-Recurrent Dynamic Neural Networks (NNs), with output neurons feedback and feed forward arrays for noisy signals, iii-Reinforcement NNs for applications with only key features, rather than a known model, iv-Spiking NNs that adjust their synapses subject to changes in the environment, and v-Genetic Algorithms for characterization and optimization. This paper presents alternative combinations and structures of novel bio-inspired, VLSIC embedded systems for detection and quantification of bio-chemical agents, and optimum performance of SPP. The overall system objectives are to develop bio-inspired/intelligent signal perception and processing systems. Presented are sample embedded, tested, and evaluated VLSICs of intelligent SPP systems for communications and Electronic-Nose.