基于自组织模糊神经网络电力系统稳定器的设计

采用一种自组织模糊神经网络设计电力系统稳定器,该稳定器能通过结构和参数的学习,克服传统模糊控制器设计过程吕存在的盲目性及拚养伤性,避免模糊控制器中模糊逻辑规则的冗余成欠缺。仿夫表明该电力系统稳定器具有良好控制性能。     

基于自组织神经网络和模糊聚类的校园无线网用户日常行为分析

针对如何分析校园无线网络数据,挖掘数据中蕴藏的学生行为,更好地辅助教学管理,本文提出了在Hadoop平台构建基于自组织神经网络(SOFM)的模糊C-均值(FCM)聚类算法。该算法采用自组织神经网络与模糊C-均值聚类算法相结合,避免了模糊C-均值聚类算法初始化不当带来的误差,目标函数中采用马氏距离,自适应的调整了数据的几何分布。考虑到无线用户数据规模庞大,采用了Hadoop平台并行运行聚类算法。实验结果表明,本文提出的算法提高了聚类结果的准确性,有效地降低了时间复杂度,分析平台为学校管理层快速有效的做出决策提供了依据,研究分析方法对其它高校有较大地参考价值。     

一种生长型自组织神经网络的聚类研究

自组织特征映射神经网络SOM(Self-Organizing Feature Maps)是一种优良的聚类工具,但其存在着一些限制,如需要预先定义网络大小、网络的收敛性较差和结构不灵活等.为了克服这些不足,在自组织神经网络理论的指导下,提出了一种基于生长型自组织神经网络的聚类方法.在无监督的情况下,该方法采用阈值控制的触发机制实现网络中神经元的生长和删除,并通过神经元权值的有效调整,以期得到数据对象的聚类结果.实验以二维空间中的数据对象为输入样本,验证了该方法的有效性和优越性.     

A Novel Self-Organizing Neural Network for Motion Segmentation

Many computer vision techniques, above all for structure from motion problems, require a segmentation of the images captured by one or more cameras. This paper deals with the segmentation based on the motion information, but can be easily extended to other cases (color, texture and so on). A new neural network, the EXIN Segmentation Neural Network (EXIN SNN) is here introduced. It is incremental, self-organizing and considers its task as the solution of a pattern recognition problem. This original approach overcomes the limits of the traditional segmentation techniques, namely the need of a spatial support for the image objects and the translation parallel to the image plane for the objects in the scene. Examples are given both for synthetic and real images.     

Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm     

一种用于非线性控制的神经网络模糊自组织控制器

本文提出一种神经网络自组织控制器，并应用于非线性跟踪控制中，为了加快模糊控制器的在线学习，文中给出了一种变的最速梯度下降学习算法，仿真结果表明，该控制是有效的。     

Adaptive Fuzzy Neural Network Control Design via a T–S Fuzzy Model for a Robot Manipulator Including Actuator Dynamics

This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an $n$-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi–Sugeno (T–S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an $n$-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional–differential control, fuzzy-model-based control, T–S-type FNN control, and robust neural fuzzy network control systems.      

Self-Organizing Transient Chaotic Neural Network for Cellular Channel Assignment

This paper presents a self-organizing transient chaotic neural network to solve the channel assignment problem, one of NP-complete problems. The proposed neural network consists of two parts. The first part is the self-organizing evolution stage, which based on the mutual inhibition mechanisms of bristle differentiation and the problem's heuristic information. The second part is the transient chaotic neural network executing stage. A significant property of the TCNN model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing in order to escape the local minima. In the proposed neural network, the first part is used to improve the quality of the obtained solutions. The simulating results have shown that the self-organizing transient chaotic neural network improves greatly performance through solving the well-known benchmark problems, especially for the Sivarajan's and Kunz's benchmark problems, while the performance is comparable with existing algorithms.     

一种基于改进T-S模糊推理的模糊神经网络学习算法

针对模糊神经网络学习算法计算量过大,在预测模型设计中提出了基于改进T-S模糊推理的模糊神经网络学习算法。主要工作如下:首先,改进T-S模糊推理方法,定义基于偏移率的T-s模糊推理方法;然后,通过将此模糊推理方法与基于合成规则的模糊推理方法及距离型模糊推理方法相比较可以看出,所提方法有较少的计算量,且比较有效;最后,在此基础上改善了模糊神经网络学习算法,并将其应用于天气预测与安全态势预测。测试结果表明,该方法明显改善了学习效率,减少了预测模型设计中的学习次数与时间复杂度,并降低了学习误差。     

Self-Organizing and Self-Evolving Neurons: A New Neural Network for Optimization

A self-organizing and self-evolving agents (SOSENs) neural network is proposed. Each neuron of the SOSENs evolves itself with a simulated annealing (SA) algorithm. The self-evolving behavior of each neuron is a local improvement that results in speeding up the convergence. The chance of reaching the global optimum is increased because multiple SAs are run in a searching space. Optimum results obtained by the SOSENs are better in average than those obtained by a single SA. Experimental results show that the SOSENs have less temperature changes than the SA to reach the global minimum. Every neuron exhibits a self-organizing behavior, which is similar to those of the self-organizing map (SOM), particle swarm optimization (PSO), and self-organizing migrating algorithm (SOMA). At last, the computational time of parallel SOSENs can be less than the SA     

A Type-2 Self-Organizing Neural Fuzzy System and Its FPGA Implementation

This paper proposes a type-2 self-organizing neural fuzzy system (T2SONFS) and its hardware implementation. The antecedent parts in each T2SONFS fuzzy rule are interval type-2 fuzzy sets, and the consequent part is of Mamdani type. Using interval type-2 fuzzy sets in T2SONFS enables it to be more robust than type-1 fuzzy systems. T2SONFS learning consists of structure and parameter identification. For structure identification, an online clustering algorithm is proposed to generate rules automatically and flexibly distribute them in the input space. For parameter identification, a rule-ordered Kalman filter algorithm is proposed to tune the consequent-part parameters. The learned T2SONFS is hardware implemented, and implementation techniques are proposed to simplify the complex computation process of a type-2 fuzzy system. The T2SONFS is applied to nonlinear system identification and truck backing control problems with clean and noisy training data. Comparisons between type-1 and type-2 neural fuzzy systems verify the learning ability and robustness of the T2SONFS. The learned T2SONFS is hardware implemented in a field-programmable gate array chip to verify functionality of the designed circuits.      

Fuzzy Inference Neural Network for Fuzzy Model Tuning

In fuzzy modeling, it is relatively easy to manually define rough fuzzy rules for a target system by intuition. It is, however, time-consuming and difficult to fine-tune them to improve their behavior. This paper describes a tuning method for fuzzy models which is applicable regardless of the form of fuzzy rules and the used defuzzification method. For this purpose, this paper proposes a fuzzy neural network model which can embody fuzzy models. The proposed model provides the functions to perform fuzzy inference and to tune the parameters for the shape of antecedent linguistic terms, the relative importance degrees of rules, and the relative importance degrees of antecedent linguistic terms in rules. In addition, to show its applicability, we perform some experiments and present the results     

联合模糊逻辑和神经网络的网络选择算法

在网络优化选择问题的研究中,针对异构网络环境下的网络选择的问题,由于网络性能存在差异,提出一种联合模糊逻辑和神经网络的自适应网络选择算法.由于新方法具有学习训练的能力,所以能够根据输出误差对模糊神经网络的隶属度函数的参数进行动态的在线调整,从而使用户选择最优的网络.最后将联合模糊逻辑和神经网络的网络选择算法与基于模糊逻辑的网络选择算法进行了比较.仿真结果表明,改进方法能有效的保证用户舒适度比率趋于期望的理想值,实现了最优的网络接入选择,减少了乒乓效应发生的次数,并且相较于不自适应调整的模糊逻辑算法有更高的用户舒适度比率.     

Evolutionary Algorithm‐Based Radial Basis Function Neural Network Training for Industrial Personal Computer Sales Forecasting

Forecasting is one of the crucial factors in applications because it ensures the effective allocation of capacity and proper amount of inventory. Because Box–Jenkins models using linear forecasting have their constraint to predict complexity in the real world, other nonlinear approaches are developed to conquer the challenge of nonlinear forecasting. With the same goal, we are proposing a hybrid of genetic algorithm and artificial immune system (HGAI) algorithm with radial basis function neural network learning for function approximation and further applying it to conduct an industrial personal computer sales forecasting exercise. In addition, five well‐known benchmark problems were used to evaluate the results in the experiment, and the newly proposed HGAI algorithm has returned better results than the Box–Jenkins models and other algorithms.     

Fuzzy Neural Network Models for Classification

In this paper, we combine neural networks with fuzzy logic techniques. We propose a fuzzy-neural network model for pattern recognition. The model consists of three layers. The first layer is an input layer. The second layer maps input features to the corresponding fuzzy membership values, and the third layer implements the inference engine. The learning process consists of two phases. During the first phase weights between the last two layers are updated using the gradient descent procedure, and during the second phase membership functions are updated or tuned. As an illustration the model is used to classify samples from a multispectral satellite image, a data set representing fruits, and Iris data set.     

一种广义模糊神经网络的参数解耦学习算法

对于强非线性系统采用分段建模十分有效,广义模糊神经网络能实现这种思想。在此基础上,给出一种模糊规则前、后件参数可分别进行学习的算法,仿真结果表明该方法拟合能力强、学习效率高。     

模糊神经网络的DNA算法训练

提出了基于DNA计算和遗传算法的DNA遗传算法,给出了DNA遗传算法的结构，讨论了遗传操作算子,利用DNA遗传算法对FNN进行学习，比采用梯度型算法和遗传算法有更高的学习精度和更快的收敛速度,该算法有全局收敛性避免了采用梯度型学习算法训练FNN时固有的局部收敛问题，同样，该算法加速了FNN的训练，能够在线应用．     

基于LabVIEW的自组织网络的实现与应用

文章介绍了自组织神经网络在故障诊断方面的应用原理,针时自组织神经网络实现问题提出了一种通过在LabVIEW调用MATLAB应用程序实现自组织神经网络的方法.并通过轴承故障诊断的实例,证明了这种方法的有效性.     

SCARA机器人自组织模糊聚类神经网络控制器

本文提出了用于SCARA机器人运动控制的自组织模糊聚类神经网络控制器.该控制器基于模糊聚类方法在学习模糊规则之前先优化训练数据,去除冗余数据并解决数据冲突问题,不但减少了神经网络的计算负担,而且生成的规则更加适合机器人运动控制.控制器主要特点是能够动态地自组织结构,学习速度快,鲁棒性强.仿真结果表明控制效果很好.     

用于非线性动力系统控制问题的自组织神经网络自适应方法

本文提出一种用自组织自学习适应思想解决非线性动力系统控制问题的新方法。在每个小区域感受野,可以把非线性系统近似展开为线性,由神经元执行控制。各神经元的凝视点,感受野和功能由自组织自学习自适应方法进行调节。大量仿真结果验证了本方法的正确性和实用性。