臺灣博碩士論文加值系統

近年來，有許多學者致力於結合各種人工智慧學習法則，以發展各種類型的智慧型機器人，智慧型機器人應具備利用感測器所得之訊息，學習在一個未知的環境中做出適當反應之能力。當環境變的複雜，學習任務也就越加困難。演化類神經網路是結合類神經網路與演化搜尋的過程之學習方法，對動態環境具有適應力，因此適合用來發展智慧型機器人。本論文研究目的，在於應用演化類神經網路使移動式機器人在未知環境中學習巡航避障。本研究提出一結合生物神經網路，與傳統網路層的概念之網路編碼方式，利用基因式指令建構網路，藉由遺傳演算法則演化改進網路，以達自我學習的能力。為驗證所提演化類神經網路之效能，以Borland C++ 程式語言完成移動式機器人之視窗模擬軟體，由模擬結果得知，此演化類神經網路已有初步之成效。

Different kind of intelligent learning rules have been applied todevelop intelligent robots recently. An intelligent robot should beable to learn to behave itself in an unknown environment using theinput information obtained from sensors. Clearly, the more complex theenvironment, the more difficult the learning task. Evolutionaryartificial neural network (EANN), a combination of artificial neuralnetwork and evolutionary search procedures, is applied in this study todevelop intelligent mobile robot due to it''s adaptability to changingenvironment. EANN is applied in this thesis to develop an intelligent mobilerobot which has the capability of learning navigation and obstacleavoidance in an unknown environment. A novel encoding, combining theconcepts of biological network and artificial neural network, isproposed to develop the EANN. With the aid of genetic algorithms, thecoded neural network''s architecture is able to learn itself. Tovalidate the performance of the proposed EANN, a GUI (graphic userinterface) mobile robot simulator written in Borland C++ is developed.The simulation results have shown the feasibility of the proposed EANN.