Neural networks for pattern discovery and optimization in signal processing and applications

The article is intended to advance both conceptual and implementation techniques of supervised and discovery-driven neural networks in noisy time varying signal processing applications. Successful neural networks and their significance in applications are based on; selection of proper theoretical algorithms for learning, appropriate selection of the sequencing of signal processing tasks, and efficient VLSI system implementation. We present a pattern discovery self organizing feature map (SOFM), followed by a recurrent dynamic neural network (RDNN) algorithm for signal representation and processing. This approach combines the benefits of RDNNs with its SOFM counter part. Preliminary designs, implementations, test results and validation of silicon-chips for each of the above neural network approach are also presented