Tracking control of a nonholonomic mobile robot using compound cosine function neural networks

The purpose of this paper is to propose a compound cosine function neural network with continuous learning algorithm for the velocity and orientation angle tracking control of a nonholonomic mobile robot with nonlinear disturbances. Herein, two neural network (NN) controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the adaptive control of the mobile robot. The neuron function of the hidden layer in the three-layer feed-forward network structure is on the basis of combining a cosine function with a unipolar sigmoid function. The developed neural network controllers have simple algorithm and fast learning convergence because the weight values are only adjusted between the nodes in hidden layer and the output nodes, while the weight values between the input layer and the hidden layer are one, i.e. constant, without the weight adjustment. Therefore, the main advantages of this control system are the real-time control capability and the robustness by use of the proposed neural network controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances which are considered as dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of nonholonomic mobile robots has real-time control capability, better robustness and higher control precision. The compound cosine function neural network provides us with a new way to solve tracking control problems for mobile robots.     

Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot

The purpose of this paper is to propose a hybrid trigonometric compound function neural network (NN) to improve the NN-based tracking control performance of a nonholonomic mobile robot with nonlinear disturbances. In the mobile robot control system, two NN controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the tracking control of the mobile robot. The neuron functions of the hidden layer in the three-layer feedforward network structure consist of the compound cosine function and the compound sine function combining a cosine or a sine function with a unipolar sigmoid function. The main advantages of this NN-based mobile robot control system are better real-time control capability and control accuracy by use of the proposed NN controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances of dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of a nonholonomic mobile robot has better real-time control capability and control accuracy than the compound cosine function NN control system of a nonholonomic mobile robot and then verify the effectiveness of the proposed hybrid trigonometric compound function NN controller for improving the tracking control performance of a nonholonomic mobile robot with nonlinear disturbances.     

基于受控Hadamard门的量子神经网络模型及算法

为提高神经网络的逼近能力,提出一种基于受控Hadamard门设计的量子神经网络模型及算法.该模型输入为多维离散序列,可用矩阵描述,行数为输入节点数,列数为序列长度.模型为3层结构,隐层为量子神经元,输出层为普通神经元.量子神经元由量子旋转门和多位受控Hadamard门组成,利用多位受控Hadamard门中目标量子位的输出向输入端的反馈,实现对输入序列的整体记忆,利用受控Hadamard门中控制位和目标位之间的受控关系获得量子神经元的输出.基于量子计算理论设计了该模型的学习算法.该模型可高效地获取输入序列的特征.实验结果表明,当输入节点数和序列长度满足一定关系时,该模型明显优于普通BP神经网络.     

一种神经网络自适应PID控制器

应用人工神经网络的原理,设计了一种神经网络的职能PID控制器。仿真结果表明,此PID控制器对非线性时不变系统有比传统的PID好的控制效果。该控制器将神经网络和PID控制规律融为一体,既具有常规PID控制器结构简单、参数物理意义明确之优点,又具有神经网络自学习、自适应之能力,控制效果明显提高。     

一类反馈过程神经元网络模型及学习算法研究

提出了一种带有反馈输入的过程式神经元网络模型,模型为三层结构,其隐层和输出层均为过程神经元。输入层完成连续信号的输入,隐层完成输入信号的空间聚合和向输出层逐点映射,并将输出信号逐点反馈到输入层;输出层完成隐层输出信号的时、空聚合运算和系统输出。在对权函数实施正交基展开的基础上给出了该模型的学习算法。仿真实验证明了该模型的有效性和可行性。     

基于SFLA优化BP神经网络的语音信号分类

BP神经网络是一种多层前馈网络,数据经过网络的输入层、隐含层逐层处理后,由输出层进行输出,通过和期望输出的对比进行反向传播,调整网络参数使输出不断逼近期望输出;在使用BP神经网络对语音特征信号进行分类的过程中,会出现BP神经网络易陷入局部最优解、学习收敛速度慢的问题;针对此问题提出一种基于SFLA优化BP神经网络权值和阀值的方法,引入SFLA算法优化网络权值和阀值,利用SFLA优化后的BP网络模型进行语音特征信号分类;仿真结果表明,经SFLA优化后的BP神经网络与未优化的神经网络相比,不仅训练速度快, 而且误差小,语音特征信号分类的正确率平均提高1.31%。     

Centroid based Multilayer Perceptron Networks

In this study we investigate a hybrid neural network architecture for modelling purposes. The proposed network is based on the multilayer perceptron (MLP) network. However, in addition to the usual hidden layers the first hidden layer is selected to be a centroid layer. Each unit in this new layer incorporates a centroid that is located somewhere in the input space. The output of these units is the Euclidean distance between the centroid and the input. The centroid layer clearly resembles the hidden layer of the radial basis function (RBF) networks. Therefore the centroid based multilayer perceptron (CMLP) networks can be regarded as a hybrid of MLP and RBF networks. The presented benchmark experiments show that the proposed hybrid architecture is able to combine the good properties of MLP and RBF networks resulting fast and efficient learning, and compact network structure.     

基于小波去噪与改进RBF神经网络的小电流接地系统故障选线方法

提出了一种基于小波去噪与改进RBF神经网络的小电流接地系统故障选线方法。将消噪后的零序电流绝对值的最大值进行归一化处理后得到故障信息矩阵,并将该矩阵作为RBF神经网络的输入;计算RBF神经网络输入层的活跃值,当活跃值在设定范围内时,RBF神经网络的隐含层与输出层自动断开,隐含层神经元分裂,待网络中权值、方差、中心值等参数自动调整后,RBF神经网络的隐含层与输出层重新连接,输出训练结果;将测试集输入到训练好的RBF神经网络,得出故障选线结果。算例分析结果表明,该选线方法不受故障相位角、接地电阻的影响,故障选线准确、可靠。     

Effects of the number of hidden nodes used in a structured-based neural network on the reliability of image classification

A structured-based neural network (NN) with backpropagation through structure (BPTS) algorithm is conducted for image classification in organizing a large image database, which is a challenging problem under investigation. Many factors can affect the results of image classification. One of the most important factors is the architecture of a NN, which consists of input layer, hidden layer and output layer. In this study, only the numbers of nodes in hidden layer (hidden nodes) of a NN are considered. Other factors are kept unchanged. Two groups of experiments including 2,940 images in each group are used for the analysis. The assessment of the effects for the first group is carried out with features described by image intensities, and, the second group uses features described by wavelet coefficients. Experimental results demonstrate that the effects of the numbers of hidden nodes on the reliability of classification are significant and non-linear. When the number of hidden nodes is 17, the classification rate on training set is up to 95%, and arrives at 90% on the testing set. The results indicate that 17 is an appropriate choice for the number of hidden nodes for the image classification when a structured-based NN with BPTS algorithm is applied.     

基于模糊神经网络的六足机器人自主导航闭环控制系统设计

针对未知环境中六足机器人的自主导航问题,设计了一种基于模糊神经网络的自主导航闭环控制算法,并依据该算法设计了六足机器人的导航控制系统．算法融合了模糊控制的逻辑推理能力与神经网络的学习训练能力,并引入闭环控制方法对算法进行优化．所设计的控制系统由信息输入、模糊神经网络、指令执行以及信息反馈4个模块组成．环境及位置信息的感知由GPS（全球定位系统）传感器、电子罗盘传感器和超声波传感器共同完成．采用C语言重建模糊神经网络控制算法,并应用于该系统．通过仿真实验,从理论上论证了基于模糊神经网络的闭环控制算法性能优于开环控制算法,闭环控制算法能够减小六足机器人在遇到障碍物时所绕行的距离,行进速度提高了6.14%,行进时间缩短了8.74%．在此基础上,开展了实物试验．试验结果表明,该控制系统能够实现六足机器人自主导航避障控制功能,相对于开环控制系统,能有效地缩短行进路径,行进速度提高了5.66%,行进时间缩短了7.25%,验证了闭环控制系统的可行性和实用性．     

Steel columns under fire—a neural network based strength model

This paper presents a strength model of steel columns under elevated temperatures using the artificial neural network. The many influencing parameters make it difficult to build an analytical steel strength model. Being a flexible model building method, the artificial neural network is an ideal tool to construct the complex relationship between the input and the output parameters accurately. A hybrid neural network, which combines the sigmoid neurons and the radial basis function neurons at the hidden layer, is proposed to better map the input–output relationship both locally and globally. The use of the genetic algorithm approach in searching the best-hidden neurons makes the hybrid neural network less likely to be trapped in local minima than the traditional gradient-based search algorithms. The genetic algorithm based hybrid neural network is applied to model the strength of steel columns under fire. The neural network results are compared with the modified Rankine formula.     

Fuzzy generalization of the counter-propagation neural network: a family of soft competitive basis function neural networks

 Based on combining neural network (NN) with fuzzy logical system (FLS), a new family of three-layer feedforward networks, called soft-competition basis function neural networks (SCBFs), is proposed under the framework of the counter-propagation (CP) network. The hidden layer of SCBFs is designed as competitive layer with soft competitive strategy. The output function of their hidden neuron is defined as basis function taking the form of fuzzy membership function. SCBFs possess the ability of functional approximation. They are fuzzy generalization of the CP network and functionally equivalent to TS-model of fuzzy logical system. Therefore, they can be regard as either a NN or a FLS. Their learning algorithms are also discussed in this paper. Finally, some experiments are given to test the performance of SCBFs.     

一种基于Walsh变换的反馈过程神经网络模型及学习算法

提出了一种带有反馈输入的过程式神经元网络模型，模型为三层结构，其隐层和输出层均为过程神经元．输入层完成连续信号的输入，隐层完成输入信号的空间聚合和向输出层逐点映射，并将输出信号逐点反馈到输入层；输出层完成隐层输出信号的时、空聚合运算和系统输出．在对权函数实施Walsh变换的基础上给出了该模型的学习算法．仿真实验证明了模型和算法的有效性．     

Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks

A direct adaptive state-feedback controller is proposed for highly nonlinear systems. We consider uncertain or ill-defined nonaffine nonlinear systems and employ a neural network (NN) with flexible structure, i.e., an online variation of the number of neurons. The NN approximates and adaptively cancels an unknown plant nonlinearity. A control law and adaptive laws for the weights in the hidden layer and output layer of the NN are established so that the whole closed-loop system is stable in the sense of Lyapunov. Moreover, the tracking error is guaranteed to be uniformly asymptotically stable (UAS) rather than uniformly ultimately bounded (UUB) with the aid of an additional robustifying control term. The proposed control algorithm is relatively simple and requires no restrictive conditions on the design constants for the stability. The efficiency of the proposed scheme is shown through the simulation of a simple nonaffine nonlinear system.     

A context layered locally recurrent neural network for dynamic system identification

This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered locally recurrent neural network (CLLRNN) for dynamic system identification. The CLLRNN is a dynamic neural network which appears in effective in the input–output identification of both linear and nonlinear dynamic systems. The CLLRNN is composed of one input layer, one or more hidden layers, one output layer, and also one context layer improving the ability of the network to capture the linear characteristics of the system being identified. Dynamic memory is provided by means of feedback connections from nodes in the first hidden layer to nodes in the context layer and in case of being two or more hidden layers, from nodes in a hidden layer to nodes in the preceding hidden layer. In addition to feedback connections, there are self-recurrent connections in all nodes of the context and hidden layers. A dynamic backpropagation algorithm with adaptive learning rate is derived to train the CLLRNN. To demonstrate the superior properties of the proposed architecture, it is applied to identify not only linear but also nonlinear dynamic systems. The efficiency of the proposed architecture is demonstrated by comparing the results to some existing recurrent networks and design configurations. In addition, performance of the CLLRNN is analyzed through an experimental application to a dc motor connected to a load to show practicability and effectiveness of the proposed neural network. Results of the experimental application are presented to make a quantitative comparison with an existing recurrent network in the literature.     

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.     

具有时变输入输出函数的反馈过程神经网络及应用

针对输入和输出均为时变函数或过程的实际系统建模和仿真问题,提出一种输入和输出均为时变函数的反馈过程神经网络模型,该模型的第1隐层对来自输入层的时变信号进行空间加权聚合和激励运算,并在将其输出传送至第2隐层的同时反馈至输入层;第2隐层完成对其时变输入的空间加权聚合、时间累积聚合和激励运算,并将其输出传送至输出层．给出了相应的学习算法,并以实例验证了该模型及其学习算法的有效性．     

Hierarchical hybrid fuzzy-neural networks for approximation with mixed input variables

Real-world systems usually involve both continuous and discrete input variables. However, in existing learning algorithms of both neural networks and fuzzy systems, these mixed variables are usually treated as continuous without taking into account the special features of discrete variables. It is inefficient to represent each discrete input variable having only a few fixed values by one input neuron with full connection to the hidden layer. This paper proposes a novel hierarchical hybrid fuzzy neural network to represent systems with mixed input variables. The proposed model consists of two levels: the lower level are fuzzy sub-systems each of which aggregates several discrete input variables into an intermediate variable as its output; the higher level is a neural network whose input variables consist of continuous input variables and intermediate variables. For systems or function approximations with mixed variables, it is shown that the proposed hierarchical hybrid fuzzy neural networks outperform standard neural networks in accuracy with fewer parameters, and both provide greater transparency and preserve the universal approximation property (i.e., they can approximate any function with mixed input variables to any degree of accuracy).     

Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks

Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies.     

改进的BP神经网络对飞机换热器结垢厚度预测

为了节约飞机维修成本,准确预测换热器结垢厚度,通过利用改进的BP神经网络预测模型,利用25组数据,建立了换热器结垢厚度与四个因素(环境温度、空调系统进口压力、初级换热器出口温度、次级换热器出口温度)之间的网络预测模型。模型包括4个输入神经元,9个隐含层神经元和1个输出层神经元。训练结果表明,改进之后的BP神经网络模型不仅克服了原始BP神经网络收敛速度慢,稳定性差的特点,还可以以较高的精度预测换热器的结垢厚度。