Abdel-Aty-Zohdy H.S., Ewing R.L.
Oakland University, US
Keywords: bio-Inspired, electronic nose, genetic algorithms, neural networks, plastic NNs
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.
Journal: TechConnect Briefs
Volume: 3, Technical Proceedings of the 2003 Nanotechnology Conference and Trade Show, Volume 3
Published: February 23, 2003
Pages: 361 - 364
Industry sectors: Advanced Materials & Manufacturing | Sensors, MEMS, Electronics
Topic: Nanoelectronics
ISBN: 0-9728422-2-5