Network的整合.doc

Fuzzy Systems與Neural Network的整合I. Introduction Fuzzy Logic Techniques Most often use verbal and Linguistic Information from experts Neural Networks Extract Information from Systems to be Learned or ControlledTo obtain the benefits of both fuzzy Systems & Neural Networks Fuzzy Systems:-Higher LevelHumanlike IF-THEN rules thinking reasoning Ease of Incorporating Expert KnowledgeNeural Networks:-Lower LevelLearning MeiliheiOptimization MeiliheiConnectionist Structures1. Neural Fuzzy Systems 為主2. Fuzzy Neural Network 為主3. Fuzzy-Neural Hybrid Systems 綜合體1. Neural Fuzzy Systems:The use of Neural Networks as fools in Fuzzy Models2. Fuzzy Neural Networks:Fuzzification of Conventional Neural Network Models3. Fuzzy-Neural Hybrid Systems:Incorporating Fuzzy Technologies and Neural Networks Into Hybrid Systems.1. Neural Fuzzy SystemsProviding Fuzzy Systems with the kind of automatic tuning methods typical of Neural Networks.But Without Altering Their Functionality (e.g. Fuzzification, Defuzzification, Inference Engine & Fuzzy Logic Base) Augmenting Numerical Processing of Fuzzy SetsSince Neural Fuzzy Systems are Inherently Fuzzy Logic Systems. They are mostly used in Control Applications2. Fuzzy Neural Networks:Retain the basic properties and architecture of neural networks and simply fuzzify some of their Elements e.g. a Crisp Neuron can become fuzzy and the response of the neuron to the lower- layer activation signal can be of a fuzzy relation type rather than a signed typeDomain knowledge becomes formalized In forms of fuzzy sets and afterward can be applied to enhance the learning algorithms of the neural networks or augment their interpretation capabilitiesSince fuzzy neural networks are inherently neural networks, they are mostly used in pattern recognition applications3. Fuzzy-Neural Hybrid Systems:Both play a key role in hybrid SystemsANFIS: Adaptive Network-Based Fuzzy Inference systemAdaptive Neuro Fuzzy Inference System自適應網路為基準的模糊推理系統f1=p1x+q1y+r1f2=p2x+q2y+r2xA1yB1xA2yB2w1w2xyA two-input first-order Sugeno fuzzy model with 2 rulesLayer 1 Layer 2 Layer 3 Layer 4 Layer 5xySNPNPA1A2B1B2w1w2w1w2w1 f1w2 f2 fx yx yEquivalent ANFIS architectureANFIS一般適用於Sugeno Fuzzy Model或Tsukamoto Fuzzy Model.2輸入1階Sugeno Fuzzy ModelRule 1: IF x is A1 and y is B1, THEN f1=p1x+q1y+r1Rule 2: IF x is A2 and y is B2, THEN f2=p2x+q2y+r2xySNPNPA1A2B1B2w1w2w1w2w1 f1w2 f2 fx yx y 第一層 第二層 第三層 第四層 第五層第一層 負責將輸入轉換成適當的模糊集合的歸屬函數值第一層Aixor ory Bi 模糊化後得到的歸屬函數值 linguistic level為目前受控的狀態歸屬函數可能為：所要調整的參數就是ai, bi, ci這些數Premise Parametersx yx y fw2 f2w1 f1w2w1w2w1B2B1A2A1NPNPSyx第二層第二層 負責取AND值，也就是求取啟動規則的強度mAi wi P wimBi各模糊變數的 wi=Ai Bi從屬函數值 相乘運算或其它的T-norm運算但一般不用min運算因為min函數無法微分就無法使用ANFIS的學習法則啟動規則的強度x yxySNPNPA1A2B1B2w1w2w1w2w1 f1w2 f2 fx y第三層第三層 將啟動規則的強度正規化w1 wi N w2各規則的 各規則在總輸出中所佔的比例啟動強度 例如：第一條規則的啟動強度為W1而其它規則的啟動強度分別為w2, w3,wn則Normalized Firing StrengthsxySNPNPA1A2B1B2w1w2w1w2w1 f1w2 f2 fx y第四層x, y第四層 個別規則的輸出計算層wi fiwi Sugeno的多項式輸出值規則的正規化啟動強度 pi, qi, ri這些參數也可能須要調整稱為Consequent Parameters.第五層 只負責將各規則的輸出做總和的工作