Abstract
People have always been interested in how neurons work in organisms, and therefore mathmeaticans built neural network algorithms with the use of mathematical method. A neural network algorithm can mimic behaviors that connect and communicate messages between neurons in organisms. By using neural network, we got the learning signal’s representative parameters that can apply to recognize and classify different signals. The theme of the thesis is ‘exploring the feasibility of training diffusion network with on-chip circuitry’. First, modify and simplify the training algorithm of Diffusion Network. The modification of training algorithm of the diffusion network is for an easier implementation in analog integrated circuit. On the other hand, simplifying the training algorithm of the diffusion network enables the design of analog integrated circuit to become easier. The most important part of training theory is getting the parameters of the training signal by using the algorithm, ‘Monte Carlo Expectation Maximization.’ The training algorithm is modified by optimization method and can be verified by the mathematic simulation software (MATLAB). Searching the range and resolution of the parameters in the different training signals helps us to understand how necessary the accuracy of parameter is for the diffusion network to be successful. The simulation work is closely related to the circuit specification, then the study explores the feasibility of training diffusion network learning processes by the Laboratory Virtual Instrumentation Engineering Workbench (LABVIEW).