Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Massively parallel artificial intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Hardware Evolution at Function Level
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Fast learning in networks of locally-tuned processing units
Neural Computation
A High-Performance, Pipelined, FPGA-Based Genetic Algorithm Machine
Genetic Programming and Evolvable Machines
A Scalable Approach to Evolvable Hardware
Genetic Programming and Evolvable Machines
An Evolvable Hardware Chip for Prosthetic Hand Controller
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
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This paper describes an evolvable hardware (EHW) system for generalized neural network learning. We have developed an ASIC VLSI chip, which is a building block to configure a scalable neural network hardware system. In our system, both the topology and the hidden layer node functions of a neural network mapped on the chips are dynamically changed using a genetic algorithm. Thus, the most desirable network topology and choice of node function (e.g. Gaussian or sigmoid) for a given application can be determined adaptively. This approach is particularly suited to applications requiring ability to cope with time-varying problems and real-time timing constraints. The chip consists of 15 Digital Signal Processors (DSPs), whose functions and interconnections are reconfigured dynamically according to the chromosomes of the genetic algorithm. Incorporation of local learning hardware increases the learning speed significantly. Simulation results on adaptive equalization in digital mobile communication are also given. Our system is two orders of magnitude faster than a Sun SS20 on the corresponding problem.