空間分割最佳化方法與多層類神經網路學習法則及其應用

Abstract

最佳化方法已經廣泛的應用在工程的設計問題上，因此本論文將發展一套新的空間分割最佳化方法。此方法可以使用在同步及非同步的平行處理器上，而能夠加快最佳化方法執行的速度。空間分割最佳化方法亦可以處理多層線性、非線性及數位式類神經網路的訓練問題。由於類神經網路本身具有平行計算及可適應性的功能，因此本論文亦利用空間分割最佳化方法，發展出一套較有效率的類神經網路學習法則，如此將可以減少類神經網路在訓練上所需要的計算時間。而一理想的類神經網路電腦必須具備硬體及學習法則的可實現性，現今由於數位科技的進步，微處理機的性能已大幅度的改進，因此本論文亦將所發展出來的多層數位式類神經網路訓練法則應用在一般數位式微控制器上，因此這種類神經網路電腦將具有較低成本及較高執行速度的特性，可以使用在一般的類神經網路應用上。

Optimization techniques are being used in a wide spectrum of industries to enhance the design of engineering systems. Therefore, this study investigates a set of new developed optimization techniques called space-decomposition optimization (SDO) algorithm. These new developed optimization techniques can be applied to synchronous and asynchronous parallel processors to enhance the optimization efficiency. They can be also applied to the training of multilayer neural networks, including linear, nonlinear, and binary neurons. Due to the adaptive nature, the neural networks offer a parallel-processing paradigm that could be user-friendlier than the conventional approaches. Although most applications demand very high throughput, most learning algorithms for neural networks are computationally intensive in nature. Therefore, efficient learning algorithms for neural networks are developed based on the SDO techniques to satisfy the demand in engineering. Digital technology has enjoyed a tremendous growth in CPU speed. An ideal digital neurocomputer provides an adaptive and flexible platform for neural network algorithms, and hardware implementation. Therefore, this study also develops efficient learning algorithms for multilayer binary neurons based on the SDO techniques. These algorithms can be easily applied to modern digital computers, such as microcontrollers. Therefore, the general-purpose digital neurocomputer can be implemented with a low-cost, high-speed, and flexible platform for neural applications.