基于动态模糊神经网络的生物工程算法研究

目前,模糊神经网络控制在控制领域已成为一个研究热点。把神经网络应用于模糊系统,可以解决模糊系统中的知识抽取问题;把模糊系统应用于神经网络,神经网络就不再是黑箱了,人类的知识就很容易融合到神经网络中。本文提出了一种新型的动态模糊神经网络的结构及其学习算法,该动态模糊神经网络的结构基于扩展的径向基网络。其学习算法的最大特点是参数的调整和结构的辨识同时进行,且学习速度快,可用于实时建模与控制。开发了相关的算法程序,最后针对实际案例进行了仿真分析。仿真结果表明,动态模糊神经网络具有学习速度快、系统结构紧凑、泛化能力强等优点。     

一种新型的动态模糊神经网络算法

为了解决模糊系统中的知识抽取问题和避免初值选择的任意性,提出一种新型的动态模糊神经网络算法.运用规则产生准则时,考虑输出误差和可容纳边界的有效半径这2个重要因素;通过分级学习法,大大提高学习的有效性,加之参数的调整只限于线性参数,没有迭代学习,因而学习速度很快,这使算法应用于实时学习成为可能;非线性参数是由训练样本和启发式方法直接决定的.利用D-FNN来进行Mackey-Glass混沌时间序列预测实验.仿真结果表明D-FNN算法的有效性和实用性.     

基于广义动态模糊神经网络的算法研究

在D-FNN算法基础上,提出了一种新的基于椭圆基函数的广义动态模糊神经网络方法.该方法不仅可以用于系统建模、辨识和控制,而且还可以用于模糊规则的自动生成或抽取.提出了一种模糊ε-完备性作为在线参数分配机制,避免初始化选择的随机性,同时,该算法不仅能对模糊规则而且能对输入变量的重要性作出评估,从而使每条规则的输入变量的宽度可以根据它对系统性能贡献的大小实施在线自适应调整.开发了相关的算法程序,最后针对实际案例进行了仿真分析,表明了该算法的有效性和高效性.     

基于模糊对向神经网络的非线性动态系统辨识器

模糊对向神经网络（ＦＣＰ）在功能上同模糊逻辑系统的ＴＳ模型是等价的,它具有神经网络和模糊逻辑系统各自的优点,因而适宜作辨识模型。     

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。     

Add-Mult型模糊神经网络

提出了一种Add-Mult型模糊神经网络模型(AMFNN)，给出了该模型的结构。根据梯度下降算法，给出了AMFNN模糊神经网络的误差反传学习算法。与6种极具代表性的模糊推理方法进行比较的结果表明，AMFNN模糊神经网络模型具有推理精度高、适用范围广、泛化能力强以及实现容易等特点，因而具有广阔的应用前景。     

新型模糊神经网络模型

分析了已有模糊神经网络模型结构与学习算法的特点,针对它们收敛速度慢、全局逼近能力差等不足,提出了一种新型的模糊神经网络模型,其在模糊化层实现了隶属函数的合成,且结构简单、推理层只有两个节点。实验结果表明该模型具有收敛速度快、全局逼近能力强的优点,具有一定的实用价值。     

一种改进型T-S模糊神经网络

对T-S模糊神经网络进行了分析,提出了一种新型T-S模糊神经网络,改进了前件网络的结构及学习算法,减少了模糊规则层的节点数,有效地克服了T-S模糊神经网络模糊规则冗余的缺点。这种新型T-S模糊神经网络具有学习算法简单、收敛速度快等优点。把该网络应用到卷取温度控制中进行仿真,得到了满意的结果。     

一种基于RBF网络提取模糊规则的算法实现

径向基函数网络和模糊推理系统在一些柔和的情况下具有等价的功能,因此可以利用神经网络的学习算法来调节模糊系统的参数,学习后的模糊系统具有自学习和自组织性,但是削弱了模糊系统的可解释性。将模糊逻辑推理与神经网络控制技术相结合,分析了一种改进的径向基函数(RBF)神经网络结构,这种模糊神经网络结构能够有效地表达模糊系统可解释性这一突出特点,也使模糊系统具有了较好的自学习和自组织能力、通过VC 实现了基于这种RBF网络结构提取模糊规则的算法,并进行了仿真实验,仿真结果表明该算法是比较有效的。     

基于修剪技术分级学习的动态模糊神经网络算法研究*

在D-FNN中采用了修剪技术,可以检测到不活跃的模糊规则并加以剔除,从而获得更为紧凑的结构。在D-FNN中,前提参数是在学习过程中自适应地进行调整。由于分级学习策略的应用,大大提高了学习的有效性,加之参数调整只限于线性参数,没有迭代学习,因而学习速度很快,这使得本算法应用于实时学习和控制成为可能。最后针对实际案例进行了仿真分析,验证了该算法的有效性和高效性。     

基于参数调整的动态模糊神经网络算法

模糊逻辑与神经网络结合形成的模糊神经网络同时具有易于表达人类知识、存储与学习分布信息的优点,基于此,提出一种基于参数调整的动态模糊神经网络算法。采用扩展卡尔曼滤波器法将全局算法划分为线性和非线性部分,线性参数由最小二乘法和滤波器法决定,非线性参数由训练样本和启发式法直接决定,线性和非线性参数可进行实时更新。仿真结果表明,该算法能保证更简洁的结构和更短的学习时间。     

Research on prediction of traffic flow based on dynamic fuzzy neural networks

Combining the advantages of the neural network and fuzzy system, this paper makes a further research on the dynamic fuzzy neural networks (D-FNN) traffic flow prediction. Instead of being in consistence with growth of the input number, the fuzzy rule number of the D-FNN increases exponentially in the whole training network structure. In particular, this method can establish a required network structure automatically. This method is applied to the traffic flow time series to analyze and compare the predicting performance of the predicting model based on the neural network method and the adaptive neural fuzzy inference system by combining with the chaos theory. The simulation result shows that this method is quite effective and can improve the predicting accuracy.     

Dynamic fuzzy neural networks-a novel approach to functionapproximation

In this paper, an architecture of dynamic fuzzy neural networks (D-FNN) implementing Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial basis function (RBF) neural networks is proposed. A novel learning algorithm based on D-FNN is also presented. The salient characteristics of the algorithm are: 1) hierarchical on-line self-organizing learning is used; 2) neurons can be recruited or deleted dynamically according to their significance to the system's performance; and 3) fast learning speed can be achieved. Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that a more compact structure with higher performance can be achieved by the proposed approach.     

A self-learning fuzzy logic controller using genetic algorithmswith reinforcements

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations     

An ART-based fuzzy adaptive learning control network

This paper addresses the structure and an associated online learning algorithm of a feedforward multilayer neural net for realizing the basic elements and functions of a fuzzy controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with traditional fuzzy control systems in network structure and learning ability. An online structure/parameter learning algorithm, FALCON-ART, is proposed for constructing FALCON dynamically. It combines backpropagation for parameter learning and fuzzy ART for structure learning. FALCON-ART partitions the input state space and output control space using irregular fuzzy hyperboxes according to the data distribution. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into “grids”. As the number of variables increases, the number of partitioned grids grows combinatorially. To avoid this problem in some complex systems, FALCON-ART partitions the I/O spaces flexibly based on data distribution. It can create and train FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or initial information. FALCON-ART can online partition the I/O spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online data     

A novel fuzzy logic system based on N-version programming

For the consideration of different application systems, modeling the fuzzy logic rule, and deciding the shape of membership functions are very critical issues due to they play key roles in the design of fuzzy logic control system. This paper proposes a novel design methodology of fuzzy logic control system using the neural network and fault-tolerant approaches. The connectionist architecture with the learning capability of neural network and N-version programming development of a fault-tolerant technique are implemented in the proposed fuzzy logic control system. In other words, this research involves the modeling of parameterized membership functions and the partition of fuzzy linguistic variables using neural networks trained by the unsupervised learning algorithms. Based on the self-organizing algorithm, the membership function and partition of fuzzy class are not only derived automatically, but also the preconditions of fuzzy IF-THEN rules are organized. We also provide two examples, pattern recognition and tendency prediction, to demonstrate that the proposed system has a higher computational performance and its parallel architecture supports noise-tolerant capability. This generalized scheme is very satisfactory for pattern recognition and tendency prediction problems     

A self-learning and tuning fuzzy logic controller based on genetic algorithms and reinforcements

This article presents a new method for learning and tuning a fuzzy logic controller automatically. A reinforcement learning and a genetic algorithm are used in conjunction with a multilayer neural network model of a fuzzy logic controller, which can automatically generate the fuzzy control rules and refine the membership functions at the same time to optimize the final system's performance. In particular, the self-learning and tuning fuzzy logic controller based on genetic algorithms and reinforcement learning architecture, which is called a Stretched Genetic Reinforcement Fuzzy Logic Controller (SGRFLC), proposed here, can also learn fuzzy logic control rules even when only weak information, such as a binary target of “success” or “failure” signal, is available. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. It is shown that the system can solve a fairly difficult control learning problem more concretely, the task is a cart–pole balancing system, in which a pole is hinged to a movable cart to which a continuously variable control force is applied. © 1997 John Wiley & Sons, Inc.     

心电图的智能识别技术

模糊逻辑、神经网络是人工智能的重要分支,它们从不同角度、在一定程度上模拟了人类智能。本文先后将模糊逻辑、神经网络以及模糊神经网络技术用于心电图识别,获得了良好的效果。在模糊识别方面,从模糊识别矩阵的建立到模糊输入向量的确定,是针对此类具体问题的多传感器模糊信息融合算法,既综合考虑了各输入变量的作用,又突出了识别的主要依据。本文还给出了神经网络识别的三种试验结果及其与模糊神经网络识别的对比。模糊神经网络既充分发挥了神经网络的学习功能,又充分发挥了模糊逻辑的推理功能,因此具有很高的识别精度。     

Intelligent process control using neural fuzzy techniques

In this paper, we combine the advantages of fuzzy logic and neural network techniques to develop an intelligent control system for processes having complex, unknown and uncertain dynamics. In the proposed scheme, a neural fuzzy controller (NFC), which is constructed by an equivalent four-layer connectionist network, is adopted as the process feedback controller. With a derived learning algorithm, the NFC is able to learn to control a process adaptively by updating the fuzzy rules and the membership functions. To identify the input–output dynamic behavior of an unknown plant and therefore give a reference signal to the NFC, a shape-tunable neural network with an error back-propagation algorithm is implemented. As a case study, we implemented the proposed algorithm to the direct adaptive control of an open-loop unstable nonlinear CSTR. Some important issues were studied extensively. Simulation comparison with a conventional static fuzzy controller was also performed. Extensive simulation results show that the proposed scheme appears to be a promising approach to the intelligent control of complex and unknown plants, which is directly operational and does not require any a priori system information.