本文選題：方案評價 + 模糊神經(jīng)網(wǎng)絡(luò)　； 參考：《組合機床與自動化加工技術(shù)》2017年11期

【摘要】：產(chǎn)品概念設(shè)計方案的準確評價是產(chǎn)品設(shè)計開發(fā)成敗的重要因素。針對當前產(chǎn)品概念設(shè)計方案評價方法的不足,結(jié)合模糊神經(jīng)網(wǎng)絡(luò)的評價方法,提出了一種基于改進遺傳算法的模糊神經(jīng)網(wǎng)絡(luò)評價方法。通過建立模糊評價模型完成網(wǎng)絡(luò)訓練,并采用將遺傳算法與BP算法相結(jié)合的多子群自適應(yīng)遺傳BP算法優(yōu)化,在全局范圍內(nèi)快速找到最優(yōu)解。并通過Matalab實現(xiàn)了對迷你型冰箱概念設(shè)計方案數(shù)據(jù)的訓練和樣本測試,預(yù)測結(jié)果驗證了該方法的準確性和實用性。
[Abstract]:The accurate evaluation of product conceptual design scheme is an important factor for the success or failure of product design and development. In view of the deficiency of the current evaluation method of product conceptual design scheme and the evaluation method of fuzzy neural network, a fuzzy neural network evaluation method based on improved genetic algorithm is proposed. The fuzzy evaluation model is established to complete the network training, and the multi-subpopulation adaptive genetic algorithm is adopted to optimize the network, which combines genetic algorithm with BP algorithm to quickly find the optimal solution in the global range. The training and sample test of conceptual design data of mini refrigerator are realized by Matalab. The prediction results verify the accuracy and practicability of this method.
【作者單位】： 四川大學制造科學與工程學院;
【基金】：國家自然科學基金資助項目(51435011) 四川省科技支撐計劃資助項目(2014GZ0124)
【分類號】：TP183

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本文編號：2077518