利用神经网络的学习功能对控制器的隶属度函数及推理规则进行修正，以提高其自适应能力。
The membership functions and the inference rules in the controller are modified using the learning functions of neural network so that the adaptability of the controller is further enhanced.
文中采用计算机自适应的最小二乘优化方法实现模糊诊断系统隶属度函数的机器自学习功能。
The least square method of computer adaptation is applied to complete the function of computer self-learning of membership function in fuzzy diagnosis system.
提出了一种隐层结构自适应学习的径向基函数网络(HSARBF)水声目标分类器。
This paper proposes a hidden layer structure adaptive radial basis function (HSARBF) classifier.
采用一种修正恒模算法(MCMA)，该算法使修正的误差函数最小并且自适应学习率由接收序列即时调整。
In the paper, a modified constant modulus algorithm (MCMA) is proposed. The proposed algorithm minimizes a modified error function and the learning-rate is multiplied by received sequences.
为了克服粒子群算法在求解多峰函数时极易陷入局部最优解的缺陷，提出一种基于自适应动态邻居广义学习的改进粒子群算法(ADPSO)。
As Particle Swarm Optimization (PSO) may easily get trapped in a local optimum, an improved PSO based on adaptive dynamic neighborhood and comprehensive learning named ADPSO was proposed.
通过一种新学习算法的导出，并结合模糊逻辑系统中的模糊基函数，给出了一种带有通用规则库的模糊滑模自适应控制器。
Universal optimization for adjustable parameters of fuzzy based function is realized by using GA and finding adjust law of parameters in general designing adaptive controller is replaced.
通过一种新学习算法的导出，并结合模糊逻辑系统中的模糊基函数，给出了一种带有通用规则库的模糊滑模自适应控制器。
Universal optimization for adjustable parameters of fuzzy based function is realized by using GA and finding adjust law of parameters in general designing adaptive controller is replaced.
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