一种模糊逻辑系统的快速学习算法

本文提出了一种模糊逻辑系统的快速学习算法．算法要求预先确定各输入变量上模 糊集合的数目及分布;模糊规则前件可以是任意形状的模糊集合,后件则必须采用单值模糊 集合;模糊推理采用乘积推理;解模糊方法采用Tsukamoto方法．算法由输入－输出数据对 提取模糊规则．模糊规则的后件采用最小二乘方法一次计算得出．本算法对目标对象的逼近 精度取决于输入参数上模糊集合的数目,数目越多,精度越高．算法所需计算量小．     

基于遗传算法的模糊PI控制器

模糊PI控制器具有鲁棒性强、控制灵活等优点,但是将其应用于纯迟延系统时超调量较大、响应速度慢。针对此提出了一种基于遗传算法的模糊PI控制器,使用遗传算法对模糊逻辑系统参数进行训练。在以往的模糊逻辑系统建立过程中,主要依靠专家知识或工作人员经验来确定其主要参数（如模糊推理规则和隶属函数参数等）,而该文利用遗传算法对样本数据进行优化来获取系统参数。在遗传算法中,将推理规则和隶属函数参数的确定结合在一起,从而确定最优的模糊逻辑系统。仿真试验结果表明,由该方法得到的控制器用于纯迟延系统具有响应快,超调量小等优点。     

基于网点的自学习模糊逻辑系统

提出网点的概念与构造模糊逻辑系统的方法，给出了易于实现的学习算法。该方法适用于模糊规则难以获得而输入输出数据可得的情况，可用于设计基于样本的模糊控制器和系统模糊建模。理论分析和数字仿真说明了该系统的正确性与实用性。     

含噪声模糊逻辑系统的参数学习算法

当采用最小方差型的误差成本函数进行输入含噪系统的参数学习时，参数不能收敛至真值，利用包含噪声方差的误差成本函数可解决此问题．本文将此误差成本函数推广到多人单出系统，将之引入到模糊逻辑系统的参数学习中，并且输入输出数据中的噪声方差也通过学习而得到，不必进行多次测量．最后通过仿真对比验证表明了该方法的有效性．     

具有前置滤波特性的非单点模糊逻辑系统

采用非单点模糊化对输入数据进行模糊处理，分析了解释了非单点模糊逻辑系统（NSFLS）的前置滤波特性，并用误差反向传播算法对相关参数进行优化，通过仿真验证以及与单点模糊逻辑系统（SFLS）的性能比较，说明非单点模糊逻辑系统具有较强的前置滤波能力，在实际工程中具有较高的应用价值。     

基于遗传算法的自学习模糊逻辑系统

利用遗传算法实现模糊逻辑系统的自学习,提出了遗传算法和模糊逻辑系统的结合方式,并针对模糊逻辑系统的特点,提出了初始种群的生成方法,较大地提高了遗传模糊逻辑系统的自学习性能。仿真结果表明,该系统对复杂的非线性系统具有较好的学习效果。     

自适应模糊逻辑系统的聚类学习算法研究

基于模糊逻辑系统一般数学模型，利用最近邻聚类学习算法对样本数据进行自适应分组，并对系统进行训练，从而使模糊逻辑系统具有自学习功能。     

模糊神经网络算法在倒立摆控制中的应用

本文利用一种可以进行结构和参数学习的模糊神经网络成功地控制一级倒立摆,该网络是一种多层前馈网络,它将传统模糊控制器的基本要件综合到网络结构中。从而使该网络既具备神经网络的低级学习能力,从而还具备模糊逻辑系统类似人的高级推理能力。因而,给定训练数据后,该网络不仅可以学习网络参数,同时还可以学习网络结构。结构学习确定了表示了模糊规则和模糊分段数的连接类型以及隐节点数目。对一级倒立摆的实际控制效果可以证明该算法的性能和实用性。     

加权模糊规则系统研究

本文讨论了基于遗传算法的模糊规则系统,设计了一种加权模糊规则系统,使用遗传算法来学习模糊集隶属函数的位置和形状参数,规则集,包括规则的数目,以及规则的权重.使用Iris数据检验了算法,得到了满意的结果.实验显示,与无权重模糊规则相比,有权重模糊规则能够形成更好的模糊系统.     

基于数据学习逆模糊模型的自适应逆模糊控制

针对在逆模糊模型控制中出现的在线滚动数据窗口计算量大和控制模型精度低等问题,提出了基于数据逆模糊学习算法,并将该算法运用到建立逆模糊模型中。首先利用建模数据在时间与空间相邻的特点,从系统积累的数据中找出与当前模态相匹配的输入数据,在保证控制模型精度的同时大大减少了计算量,然后采用自适应算法在线调节系统模型参数,实现非线性系统的实时跟踪控制。该方法提高了系统控制精度与计算效率。仿真结果证明了该方法的有效性。     

Compensatory neurofuzzy systems with fast learning algorithms

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of a cart-pole balancing system and nonlinear system modeling have shown that: 1) the compensatory neurofuzzy system can effectively learn commonly used fuzzy IF-THEN rules from either well-defined initial data or ill-defined data; 2) the convergence speed of the compensatory learning algorithm is faster than that of the conventional backpropagation algorithm; and 3) the efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree.     

模糊逻辑系统的快速构造与优化

提出一种直接利用均匀分布于待逼近系统输入空间的Ｉ／Ｏ数据,快速构造满足一定精度要求的模糊逻辑系统方法,并从理论上证明了该方法的可行笥。在此基础上采用一种新型的ＧＡ＋ＢＰ混合自治对模糊逻辑系统进行优化,以求用最少的规则数实现满意的精度。数字仿真结果表明这种快速构造和优化方法是可行和高效的。     

A hybrid coevolutionary algorithm for designing fuzzy classifiers

Rule learning is one of the most common tasks in knowledge discovery. In this paper, we investigate the induction of fuzzy classification rules for data mining purposes, and propose a hybrid genetic algorithm for learning approximate fuzzy rules. A novel niching method is employed to promote coevolution within the population, which enables the algorithm to discover multiple rules by means of a coevolutionary scheme in a single run. In order to improve the quality of the learned rules, a local search method was devised to perform fine-tuning on the offspring generated by genetic operators in each generation. After the GA terminates, a fuzzy classifier is built by extracting a rule set from the final population. The proposed algorithm was tested on datasets from the UCI repository, and the experimental results verify its validity in learning rule sets and comparative advantage over conventional methods.     

A self-organizing fuzzy logic controller for dynamic systems usinga fuzzy auto-regressive moving average (FARMA) model

The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules, however, the proposed new fuzzy logic controller needs no expert in making control rules, Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are stored in the fuzzy rule space and updated online by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum     

A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance

Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system.     

Fuzzy rule-based networks for control

The authors present a method for learning fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: first, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an information-theoretic approach for induction of rules from discrete-valued data; and, finally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backer-upper control system     

基于自适应神经元学习模糊控制规则

本文给出了利用自适应神经元学习、修改模糊控制规划的新方法，该方法可以学习与当前控制过程输出性能有关的在过去起作用的控制规划，可以随过程环境变化自动调整控制规划，以改善过程输出性能。     

Learning and tuning fuzzy logic controllers through reinforcements

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.     

Fuzzy logic techniques for mobile robot obstacle avoidance

This paper is concerned with the problem of reactive navigation for a mobile robot in an unknown clustered environment. We will define reactive navigation as a mapping between sensory data and commands. Building a reactive navigation system means providing such a mapping. It can come from a family of predefined functions (like potential fields methods) or it can be built using ‘universal’ approximators (like neural networks). In this paper, we will consider another ‘universal’ approximator: fuzzy logic. We will explain how to choose the rules using a behaviour decomposition approach. It is possible to build a controller working quite well but the classical problems are still there: oscillations and local minima. Finally, we will conclude that learning is necessary for a robust navigation system and fuzzy logic is an easy way to put some initial knowledge in the system to avoid learning from zero.