基于模糊神经网络的网络业务分类研究

该文利用神经网络的自学习能力和模糊逻辑的动态性和及时性等特点,将模糊逻辑和神经网络有机地结合起来,构造出了四层模糊神经网络,并用训练神经网络的相应学习算法训练网络,将该模型用于网络业务源特征提取与分类的研究中,并与单纯的神经网络算法相比较。计算机仿真结果表明,模糊神经网络方法比神经网络算法更优越,该文的研究结果为解决网络业务源特征提取与分类奠定了基础。     

补偿模糊神经网络自适应逆控制的仿真研究

该文应用的补偿模糊神经网络（CFNN）是结合补偿模糊逻辑和神经网络的混合系统。由于引入补偿神经元使网络容错性更高,系统更稳定;同时模糊运算采用动态的、全局优化运算,并在神经网络学习算法中动态优化补偿模糊运算,使网络更适应,训练速度更快。将补偿模糊神经网络与白适应逆控制原理结合应用到某位置伺服系统噪声消除控制中,并同用BP网络,传统PID控制和常规模糊神经网络控制效果比较来证明此方法的优越性。仿真结果表明补偿模糊神经网络自适应逆控制在缩短训练时间,提高控制精度等方面都有显著改善。     

一种基于模糊神经网络的数据挖掘算法

提出了一种基于模糊神经网络的数据挖掘算法,把模糊理论和神经网络结合起来构造、训练模糊神经网络,弥补了神经网络结构复杂、网络训练时间长、结果表示不易理解等不足。经过模糊神经网络的建立和训练达到精度要求,实现了运用模糊神经网络方法从数据库中提取知识的目标。     

一款可编程语音处理器的设计与应用

提出了一种基于模糊神经网络的数据挖掘算法,把模糊理论和神经网络结合起来构造、训练模糊神经网络,弥补了神经网络结构复杂、网络训练时间长、结果表示不易理解等不足.经过模糊神经网络的建立和训练达到精度要求,实现了运用模糊神经网络方法从数据库中提取知识的目标.     

一种粗糙模糊神经网络模型的研究及应用

基于粗糙集和模糊理论研究粗糙模糊神经网络的设计,分析并比较粗糙模糊神经网络和其它神经网络的不同。在提取虚拟场景图像的音质效果参数的实验中,验证了粗糙模糊神经网络的有效性,同时发现其在网络结构和收敛性方面的优势。     

模糊神经网络在电子鼻系统定量识别中的应用

提出并实现了两种类型的模糊神经网络用于电子鼻系统的定量识别。阐述了模糊神经网络用于电子鼻系统定量识别的基本原理,简要介绍了两种模糊神经网络的结构特点并进行了比较,并通过具体实例说明了模糊神经网络用于电子鼻系统的定量识别的可行性以及识别的效果。在该系统中,基于径向基网络模型的模糊神经网络的预测性能在整体上要优于基于感知器模型的模糊神经网络。     

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。     

基于模糊神经网络的粗糙集在股市预测中的应用

提出在模糊神经网络中使用粗糙集理论进行网络的设计.在模糊神经网络中引入粗糙集理论,不仅可以去除模糊神经网络中输入层的冗余神经元而且可以确定隐含层神经元的数目,从而使模糊神经网络具有更准确的逼近收敛能力和较高的精度.最后应用于股票市场,在股票买卖时机预测中取得了良好的效果.     

自适应模糊神经网络控制器在电阻加热炉中的应用

提出一种自适应模糊神经网络控制器,着重讨论了自适应模糊神经网络的混合学习算法和自适应动量解耦的最速下降法。给出了适于非线性时滞、基于径向基函数网络和自适应模糊神经网络控制器的控制方案,并把它用在电阻加热炉中。实际应用表明,模糊神经网络控制器具有良好的控制效果。     

一般输入的折线模糊神经网络对模糊函数的通用逼近

首先基于一种扩展原理和模糊算术得到一类前向模糊神经网络--折线模糊神经网络.当模糊神经网络的输入为一般模糊数,激励函数为单调连续型Sigmoidal函数时,分析网络的拓扑结构及相关性质.然后证明该折线模糊神经网络能作为模糊连续函数的通用逼近器,其等价条件是模糊函数的递增性.因此关于输入为一般模糊数的折线模糊网络是否为通用逼近器的问题得到解决,且折线模糊神经网络的应用范围将进一步扩大.     

基于分形和FNN的水轮机组振动故障在线诊断*

水轮发电机组的故障诊断具有模糊性和耦合性,提出一种基于模糊神经网络FNN的水轮发电机组振动故障在线诊断方法。首先,对反映转子振动状态的轴心轨迹用分形维数提取其结构特征,实现图形量化,以便FNN在线识别;接着,以6种典型振动故障为研究对象,在总结了包括轴心轨迹在内4类共14种故障征兆的基础上,分析各故障征兆的模糊属性,给出它们的模糊处理;然后,建立一种六层的前向FNN映射征兆到故障间的模糊推理,并给出学习算法修正网络参数;FNN通过自学习可保证良好的在线诊断精度。实例分析结果验证了其可行性。     

一类模糊神经网络结构的混沌优化设计

基于混沌变量，提出一种关于模糊神经网络结构的优化设计方法。将混沌变量引入模糊神经网络结构和参数的优化搜索中，使得模糊神经网络的规则数以及所有参数都处于混沌状态中，根据性能指标来寻找一个较优的网络。在线优化采用最小二乘法对去模糊化部分的权参数进行实时修正。仿真实验表明，基于混沌优化的模糊神经网络结构精简，控制精度高。     

自适应小生态遗传算法的理论分析和加速技术

提出了联赛选择和相似个体概率替换的自适应小生态遗传算法，建立了小生态生长的动力学模型．平衡态理论分析和仿其实验表明，概率联赛小生态技术选择能够形成和维持稳定的子种群．提出了种群聚类分割和单纯形搜索的并行局部搜索算于，定性地分析了其搜索性能．对复杂多峰问题的优化结果表明，结合概率联赛选择和并行局部搜索算子的小生态遗传算法不但能够快速可靠地收敛到全局最优解，且能并行地搜索到多个局部最优解，其收敛速度和全局收敛可靠性均显著地优于简单遗传算法和其它小生态方法．     

Fuzzy neural network quadratic stabilization output feedback control for biped robots via H/sub /spl infin// approach

A novel fuzzy neural network (FNN) quadratic stabilization output feedback control scheme is proposed for the trajectory tracking problems of biped robots with an FNN nonlinear observer. First, a robust quadratic stabilization FNN nonlinear observer is presented to estimate the joint velocities of a biped robot, in which an H/sub /spl infin// approach and variable structure control (VSC) are embedded to attenuate the effect of external disturbances and parametric uncertainties. After the construction of the FNN nonlinear observer, a quadratic stabilization FNN controller is developed with a robust hybrid control scheme. As the employment of a quadratic stability approach, not only does it afford the possibility of trading off the design between FNN, H/sub /spl infin// optimal control, and VSC, but conservative estimation of the FNN reconstruction error bound is also avoided by considering the system matrix uncertainty separately. It is shown that all signals in the closed-loop control system are bounded.     

Intelligent forecasting system based on integration of electromagnetism-like mechanism and fuzzy neural network

Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evolution of the learning algorithms for the connection weights is also a very important. Researchers have proposed gradient descent methods such as the back propagation algorithm and evolution methods such as genetic algorithms (GA) for training FNN connection weights. In this paper, we integrate a new meta-heuristic algorithm, the electromagnetism-like mechanism (EM), into the FNN training process. The EM algorithm utilizes an attraction–repulsion mechanism to move the sample points towards the optimum. However, due to the characteristics of the repulsion mechanism, the EM algorithm does not settle easily into the local optimum. We use EM to develop an EM-based FNN (the EM-initialized FNN) model with fuzzy connection weights. Further, the EM-initialized FNN model is used to train fuzzy if–then rules for learning expert knowledge. The results of comparisons done of the performance of our EM-initialized FNN model to conventional FNN models and GA-initialized FNN models proposed by other researchers indicate that the performance of our EM-initialized FNN model is better than that of the other FNN models. In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models.     

基于模糊神经网络的苗木地径定位方法研究

为了实现针叶苗木分级特征的提取 ,提出了基于模糊神经网络 (FNN)的地径自动搜索定位 (RC- ASL)方法 ,同时提出了针叶苗木图象行像素特征向量的构造方法 ,并应用相应的隶属函数实现了各特征量的模糊化过程 .经过网络的学习和训练 ,得到了用于实现 RC- ASL 方法的 FNN结构 .实验结果表明 ,该方法的定位精度能够满足实际应用的要求     

Adaptive fuzzy APSO based inverse tracking-controller with an application to DC motors

This paper introduces the use of the adaptive particle swarm optimization (APSO) for adapting the weights of fuzzy neural networks (FNN) on line. The fuzzy neural network is used for identification of the dynamics of a DC motor with nonlinear load torque. Then the motor speed is controlled using an inverse controller to follow a required speed trajectory. The parameters of the DC motor are assumed unknown as well as the nonlinear load torque characteristics. In the first stage a nonlinear fuzzy neural network (FNN) is used to approximate the motor control voltage as a function of the motor speed samples. In the second stage, the above mentioned approximator is used to calculate the control signal (the motor voltage) as a function of the speed samples and the required reference trajectory. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using adaptive particle swarm optimization (APSO). The APSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Simulation results are presented to prove the effectiveness of the proposed control technique in achieving the tracking performance.     

A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing

This paper proposes a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for dynamic system processing. An RSEIT2FNN incorporates type-2 fuzzy sets in a recurrent neural fuzzy system in order to increase the noise resistance of a system. The antecedent parts in each recurrent fuzzy rule in the RSEIT2FNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. The antecedent part of RSEIT2FNN forms a local internal feedback loop by feeding the rule firing strength of each rule back to itself. The TSK-type consequent part is a linear model of exogenous inputs. The RSEIT2FNN initially contains no rules; all rules are learned online via structure and parameter learning. The structure learning uses online type-2 fuzzy clustering. For the parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm to improve learning performance. The antecedent type-2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm. The RSEIT2FNN is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise-free and noisy conditions. Comparisons with type-1 recurrent fuzzy neural networks validate the performance of the RSEIT2FNN.     

基于模糊神经网络的飞机某系统故障诊断研究

提出一种基于模糊神经网络的飞机某系统故障诊断方法。利用改进的模糊C均-值聚类算法进行结构辨识,从而自动获得模糊规则库,并得到模糊模型的初始参数;然后生成与之相匹配的初始模糊神经网络,并通过学习算法训练网络来进行参数辨识,得到一个精确的模糊模型。将该系统地面实测数据作为样本数据,建立起了基于模糊神经网络的飞机某系统故障诊断模型。最后对该模型进行测试与分析,结果表明该方法具有抗噪、抗敏感、诊断准确度高等优点。     

基于模糊神经网络开关磁阻电动机高性能转矩控制

开关磁阻电动机由于其转矩是各相电流与转子位置角的高度非线性函数, 传统控制方法难以对其达到有效的控制. 应用模糊神经网络对开关磁阻电动机静态转矩特性逆模型进行离线学习, 学习完成之后, 在转矩分配函数的基础上, 实时在线优化出期望转矩所需要的相电流波形, 从而实现开关磁阻电动机的转矩线性、解耦、无脉动控制. 计算机仿真结果证明了这种方法的有效性.