Classifier construction based on the fuzzy cognitive map
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摘要: 提出了一種新的模糊認知圖分類器模型構造方法,它包括構建流程、激活函數、推理規則和學習方法等核心構件.模型利用提出的動態交叉變異算子自適應遺傳進化過程,實現種群間自動調節和自動適應.仿真實驗表明:本文提出的模型增強了局部隨機搜索能力,加強了算法的全局收斂能力,與其他經典分類方法相比,不但性能較好,而且具有較強的抗噪能力,從而具有更強的魯棒性.Abstract: A novel construction method of classifier models based on the fuzzy cognitive map was proposed,which consists of model structure,activation functions,inference rules and learning algorithms.The model employs dynamically self-adaptive crossover and mutation operators to automatically adjust the evolution process within populations.Simulation experiments prove that the model enhances the capabilities of local random search and global convergence.Compared with other classical classification algorithms,the model not only shows a better classification performance,but also has powerful noise-immune ability which renders it robust.
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Key words:
- fuzzy cognitive map /
- classifiers /
- classification /
- fuzzy set theory /
- learning algorithms
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