Research and Developments in Neural Networks
Pp. 217-235 (19)
Two categories of ANN systems are able to model any intelligent Expert in
great detail. These are Bayesian network, and neuromorphic network. Both are
hampered by lack of adequate resources and lack of human knowledge. Research and
development on these two categories of ANN systems is the subject of this chapter.
The first section of chapter 11, on Bayesian network, specifically describes Hybrid
Monte Carlo (HMC) and associated algorithms, after which areas of possible
researches are highlighted. The second section is on neuromorphic network. It presents
the current state of industrial development. The chapter has taken care to omit those
conceptual developments which may not be achievable in near future. Illustration of the
recent neuromorphic design has been given in concluding the second section. The
chapter has provided research and development information resources on two advanced
Average error, CMOS, Comlex-conjugate Eigenvalue, Efficacy,
Hamiltonian system, Harmonic oscillator, Hybrid Monte Carlo (HMC), Leapfrog
algorithm, Long-Term Depression (LTD), Long-Term Potentiation (LTP),
Markov-Chain Monte Carlo (MCMC), Multi-dimensional matrix, Omyleyan
integrator, Pulse-Width Modulation ( PWM), Random-number generator,
Shadow Hamiltonian, Simplectic, Spike-Timing-Dependent-Plasticity (STDP)
computation, Titanium dioxide, Verlet velocity.
University of Kent, United Kingdom.