Identification of time-series signals using a dynamic neural network with GA-based training

We propose a dynamic neural network (DNN) that realizes a dynamic property and has a network structure with the properties of inertia, viscosity, and stiffness without time-delayed input elements, and a training algorithm based on a genetic algorithm (GA). In a previous study, we proposed a modified training algorithm for the DNN based on the error back-propagation method. However, in the previous method it was necessary to determine the values of the DNN property parameters by trial and error. In the newly proposed DNN, the GA is designed to train not only the connecting weights but also the property parameters of the DNN. Simulation results show that the DNN trained by the GA obtains good performance for time-series patterns generated from an unknown system, and provides a higher performance than the conventional neural network. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, 0ita, Japan, February 4–6, 2005     

双摆旋转起重机轨迹生成

虽然起重机系统的荷载摆动呈现出双摆特性比单摆特性更加接近于实际生产,但是,在这种情况下,系统特性分析和控制器的设计难度将大大增加.为此,本文提出一种基于S型曲线轨迹生成法实现旋转起重机两级残留摆角的抑制.首先根据拉格朗日运动方程推导出双摆旋转起重机的数学模型,再采用干扰观测器对该模型中的摩擦项、各子系统之间的耦合项进行补偿从而获得线性模型.由于各子系统中的一、二级摆角仍然是耦合的,因此采用模态分析对其进一步简化从而获得各自子系统的线性解耦模型.其次,分别为各子系统设计S型曲线轨迹,相关参数可通过求解代数方程获得.最后,数值仿真验证该方法的有效性.     

Global optimisation in neural network training

This paper presents a new neural network training scheme for pattern recognition applications. Our training technique is a hybrid scheme which involves, firstly, the use of the efficient BFGS optimisation method for locating minima of the total error function and, secondly, the use of genetic algorithms for finding a global minimum. This paper also describes experiments that compare the performance of our scheme with three other hybrid schemes of this kind when applied to challenging pattern recognition problems. Experiments have shown that our scheme gives better results than others.     

遗传算法优化的神经网络无速度传感器DTC系统

利用异步电动机的定子电压方程和磁链方程构造神经网络速度观测器来获得电动机的转速,观测器简单易行,通过遗传算法来优化神经网络的权值,使神经网络的权值达到最优。最后通过Matlab仿真,验证了系统设计的有效性和可行性。     

基于模糊神经网络的自适应控制系统的设计

自适应控制系统往往结合神经网络技术和模糊理论来实现规则节点和隶属度函数调整。但是这种系统的运行过程往往是顺序的,自适应过程慢。此外,在网络结构中往往存在冗余节点,加大了计算量,降低了控制反应速度。针对以上问题本文设计1个新的模糊神经网络控制系统(FNCC),FNCC在结构学习中引入了减少规则节点的操作,降低了由于过量计算所带来的时间滞后。同时,此系统的参数学习与网络结构学习同步进行,降低了由于顺序操作所带来的时间滞后。通过研究得出FNCC具有以下特点:(1)无需预知系统的模型,(2)无限制的结构设计。在研究中我们将此系统应用到一非线性系统上,通过仿真结果来验证FNNC的可行性和准确性。     

基于神经网络的解耦控制方法

本文介绍了BP算法的基本原理及其实现步骤,并将BP算法应用于神经网络解耦器和PID神经网络的训练中,即本文中各个神经网络的训练算法均采用BP算法,提出了一种神经网络在线解耦控制算法,即将神经网络解耦和神经网络PID控制两者结合,对系统进行解耦控制。将解耦与控制结合,既避免了单独采用自适应PID控制时控制效果不佳的问题,又避免了单独采用解耦时原有控制器不能适应变化后的对象问题。最后对一组双输入双输出耦合系统进行了仿真研究。     

Adaptive neural network hierarchical sliding mode control for six degrees of freedom overhead crane

This paper presents an effective control method for three-dimensional (3D) overhead cranes with six degrees of freedom (DOF). Two payload swings and an axial payload oscillation should be minimized besides driving the bridge, trolley, and hoisting drum to bring the payload to the desired position in space. First, a novel 3D-6DOF crane model is developed, where the sixth degree of freedom is axial cargo oscillation that has never been considered in previous studies. A controller is then designed using the hierarchical sliding mode control method. Moreover, a radial basis function neural network (RBFNN) is used to approximate the system's unknown dynamic model accurately. According to the Lyapunov principle, a control law and an updated law for the neural network's weight matrices are designed to ensure the stability of the closed-loop system. Simulation results on Matlab software show the proposed approach's effectiveness, such as smaller swing, minor axial oscillation, and precise position as desired.     

GA-based nonlinear predictive switching control for a boiler-turbine system

In this paper,the problem of designing a controller for a highly coupled constrained nonlinear boilerturbine system is addressed with a predictive controller.First,a nonlinear predictive control is imp...     

Multiprocessor scheduling and neural network training methods using shuffled frog-leaping algorithm

In this paper, we designed novel methods for Neural Network (NN) and Radial Basis function Neural Networks (RBFNN) training using Shuffled Frog-Leaping Algorithm (SFLA). This paper basically deals with the problem of multi-processor scheduling in a grid environment. We, in this paper, introduce three novel approaches for the task scheduling problem using a recently proposed Shuffled Frog-Leaping Algorithm (SFLA). In a first attempt, the scheduling problem is structured as a problem of optimization and solved by SFLA. Next, this paper makes use of SFLA trained Artificial Neural Network (ANN) and Radial Basis function Neural Networks (RBFNN) for the problem of task scheduling. Interestingly, the proposed methods yield better performance than contemporary algorithms as evidenced by simulation results.     

Nonlinear system identification using optimized dynamic neural network

In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme—Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.