一种网络流量预测的小波神经网络模型

结合小波变换和人工神经网络的优势，建立一种网络流量预测的小波神经网络模型。首先对流量时间序列进行小波分解，得到小波变换尺度系数序列和小波系数序列，以系数序列和原来的流量时间序列分别作为模型的输入和输出，构造人工神经网络并且加以训练。用实际网络流量对该模型进行验证，结果表明，该模型具有较高的预测效果。     

Development of PI training algorithms for neuro-wavelet control on the synchronization of uncertain chaotic systems

This paper investigates a neuro-wavelet control (NWC) system to address the problem of synchronization control of uncertain chaotic systems. In this NWC system, a wavelet neural network (WNN) controller is the principal tracking controller designed to mimic the perfect control law and an auxiliary compensation controller is used to recover the residual approximation error so that the favorable synchronization can be achieved. Moreover, the proportional-integral (PI) training algorithms of the control system are derived from the Lyapunov stability theorem, which are utilized to update the adjustable parameters of WNN controller on-line for further assuring system stability and obtaining a fast convergence. In addition, to relax the requirement of unknown uncertainty bound, a bound estimation law is derived to estimate the uncertainty bound. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control strategy. The simulation results demonstrate that the proposed NWC with PI training algorithms can synchronize the chaotic systems more accurately than the other control strategies.     

Artificial neural network and wavelet neural network approaches for modelling of a solar air heater

This paper reports on a modelling study of new solar air heater (SAH) system by using artificial neural network (ANN) and wavelet neural network (WNN) models. In this study, a device for inserting an absorbing plate made of aluminium cans into the double-pass channel in a flat-plate SAH. A SAH system is a multi-variable system that is hard to model by conventional methods. As regards the ANN and WNN methods, it has a superior capability for generalization, and this capability is independent on the dimensionality of the input data’s. In this study, an ANN and WNN based methods were intended to adopt SAH system for efficient modelling. To evaluate prediction capabilities of different types of neural network models (ANN and WNN), their best architecture and effective training parameters should be found. The performance of the proposed methodology was evaluated by using several statistical validation parameters. Comparison between predicted and experimental results indicates that the proposed WNN model can be used for estimating the some parameters of SAHs with reasonable accuracy.     

Intelligent transportation control system design using wavelet neural network and PID-type learning algorithms

In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) and proportional-integral-derivative-type (PID-type) learning algorithms is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and an auxiliary compensation controller. The neural controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of an ideal total sliding-mode control (TSMC) law. The PID-type learning algorithms are derived from the Lyapunov stability theorem, which are utilized to adjust the parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H_∞ control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved. Finally, to investigate the effectiveness of the proposed control strategy, it is applied to control a marine transportation system and a land transportation system. The simulation results demonstrate that the proposed WNN-based ITCS with PID-type learning algorithms can achieve favorable control performance than other control methods.     

基于小波神经网络的板形板厚综合系统逆控制

针对板形板厚综合系统具有强耦合、非线性、含纯滞后环节的特点,提出一种基于小波神经网络的逆控制方案.利用两个结构相同的小波神经网络构造Smith预估器,预估器的输入参数与时延阶次无关,能较好地解决小波神经网络对维数较为敏感的问题.采用神经网络逆控制的思想设计小波神经网络控制器,引入多步预测性能指标函数对控制器权值进行在线训练.仿真研究表明,该控制方案具有较快的响应速度和良好的动态性能.     

Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates

This paper proposes an indirect adaptive control method using self recurrent wavelet neural networks (SRWNNs) for dynamic systems. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). However, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN can store the past information of wavelets. In the proposed control architecture, two SRWNNs are used as both an identifier and a controller. The SRWNN identifier approximates dynamic systems and provides the SRWNN controller with information about the system sensitivity. The gradient-descent method using adaptive learning rates (ALRs) is applied to train all weights of the SRWNN. The ALRs are derived from discrete Lyapunov stability theorem, which are applied to guarantee the convergence of the proposed control system. Finally, we perform some simulations to verify the effectiveness of the proposed control scheme.     

Adaptive neural control for a class of nonlinearly parametric time-delay systems

In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.     

基于小波神经网络的电容称重传感器非线性补偿研究

为了解决电容称重传感器的非线性问题,提出了补偿其非线性的小波神经网络方法。该方法以电容称重传感器实验数据为基础,通过小波神经网络训练来确定传感器非线性补偿网络。介绍电容称重传感器非线性补偿原理,分析网络的拓扑结构,给出网络参数训练方法。结果表明,采用小波神经网络进行电容称重传感器非线性补偿具有好的鲁棒性,网络训练速度快、精度高,并能在线补偿,在测试领域有实用价值。     

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future dynamic behavior of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentiation (AD) techniques. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve the online optimization problem in the predictive controller. The proposed neural network and the nonlinear predictive controller were tested on an evaporation case study. A good model fitting for the nonlinear plant is obtained using the new method. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. The CTRNN trained is used as an internal model in a predictive controller and results in good performance under different operating conditions.     

参数可变系统时间序列短期预测方法

时间序列预测是一类非常重要的问题,但基本上局限于参数不可变问题的研究,而对实际问题中经常出现的更重要的参数可变系统的预测,由于构成几乎所有已有预测技术基础的Taken嵌入定理不再成立,所以这方面的研究成果极少.使用一种将(多)小波变换与反向传播神经网络相结合的新型网络结构--(多)小波神经网络,尝试对参数可变时间序列的预测.因为(多)小波神经网络的误差函数是一个凸函数,这在一定程度上可以避免经典神经网络容易陷入局部极小、收敛速度慢等问题.对著名的Ikeda参数可变系统的实验表明,多小波神经网络的预测性能较单小波神经网络要好,而单小波神经网络的性能较BP网要好.因此,该方法不失为时间可变系统预测的一种好的推荐.     

基于逆系统的三自由度直升机内模控制设计

三自由度直升机模型系统是一个典型的非线性、高阶次、多变量、强耦合的多入多出系统,通过数学推导很难获得其逆模型,利用神经网络对任意连续函数很强的逼近能力与逆系统的思想相结合的方法获取三自由度直升机模型系统的逆模型,同时也将一个多入多出、强耦合的非线性系统转化为几个相对独立的单入单出的线性系统。采用内模控制的方法设计控制器,实现对高度角和横侧角的跟踪控制,MATLAB仿真结果表明该方法具有较好的控制效果,半实物仿真也说明该方法的可行性。     

小波神经网络在房地产价格指数预测中的应用

随着房地产价格指数的作用充分显现,探求预测房地产价格指数的有效方法是需深入研究的方向。该文以中房上海住宅价格指数为例,首先对房地产价格指数序列性质进行分析,表明房地产价格指数是具有非线性特征的非平稳时间序列。采用小波神经网络对房地产价格指数进行预测,并将预测结果与指数平滑法和RBF神经网络预测做了对比。采用MATLAB对拟合和预测过程进行仿真。结果指标表明,在大样本数据的情况下,采用小波神经网络对房地产指数进行预测能够获得较好的效果。     

基于改进粒子群优化算法的新型小波神经网研究

本文提出了改进的粒子群优化算法（Improved Particle Swarm Optimization,IPSO）的新型BP 小波 神经网络,并且对非线性辨识问题进行了仿真实验．实验结果表明,基于改进的粒子群优化算法的BP 小波网 络不仅具有小波分析良好的局部特性以及神经网络的学习、分类能力,而且具有粒子群优化算法全局快速寻 优的特点．与简单的粒子群优化算法相比,该方法在收敛性和稳定性方面都有了较明显的提高,验证了它的 合理性和有效性．     

采用小波神经网络的惯导系统初始对准

小波神经网络具有收敛速度快、结构简单、计算量少等优点。该文首先论证了小波神经网络的理论基础,然后给出了小波神经网络的参数估值方法及隐层小波元个数的确定依据,并将其用于惯导初始对准中。仿真结果表明,该方法能有效地实现初始对准的状态估计,既得到了与卡尔曼滤波器相当的精度,又减少了卡尔曼滤波的过渡时间,提高了系统的实时性及收敛性。     

基于WNN的BP算法的多传感器数据融合研究

小波神经网络（WNN）是将小波理论和神经网络理论结合起来的一种神经网络,有较强的函数学习能力和推广能力及广阔的应用前景。采用基于WNN的BP权值平衡算法对多传感器测量的结果进行特征级的数据融合,融合结果提供给决策级判断。该融合算法避免了BP网络收敛速度慢,易产生局部最优解等缺点,提高了学习的速度、精度。仿真结果表明了该方法的有效性。     

小波神经网络在飞控系统辨识中的应用研究

选择以sigmoid函数为基础的小波基波函数构造了一个小波神经网络,利用小波网络对复杂的飞控系统对象进行在线辨识研究,仿真结果表明小波神经网络基本满足某型飞机飞控系统在线辨识的要求。     

小波网络在航空发动机状态监测中的应用研究

为准确监测航空发动机的状态以保障飞行安全,需要对航空发动机这个复杂系统建立精确模型,将人工神经网络很强的非线性映射能力与小波分析特有的时频分析能力相结合可以对复杂的非线性系统进行系统辨识;选择以sigmoid函数为基础的小波基波函数作为神经网络神经元的激励函数,构造了一个三层小波神经网络,利用该小波神经网络对航空发动机转动状态进行系统辨识研究,仿真结果表明小波神经网络能对某型飞机航空发动机转动状态进行准确辨识.     

Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks

In this study, differential evolution algorithm (DE) is proposed to train a wavelet neural network (WNN). The resulting network is named as differential evolution trained wavelet neural network (DEWNN). The efficacy of DEWNN is tested on bankruptcy prediction datasets viz. US banks, Turkish banks and Spanish banks. Further, its efficacy is also tested on benchmark datasets such as Iris, Wine and Wisconsin Breast Cancer. Moreover, Garson’s algorithm for feature selection in multi layer perceptron is adapted in the case of DEWNN. The performance of DEWNN is compared with that of threshold accepting trained wavelet neural network (TAWNN) [Vinay Kumar, K., Ravi, V., Mahil Carr, & Raj Kiran, N. (2008). Software cost estimation using wavelet neural networks. Journal of Systems and Software] and the original wavelet neural network (WNN) in the case of all data sets without feature selection and also in the case of four data sets where feature selection was performed. The whole experimentation is conducted using 10-fold cross validation method. Results show that soft computing hybrids viz., DEWNN and TAWNN outperformed the original WNN in terms of accuracy and sensitivity across all problems. Furthermore, DEWNN outscored TAWNN in terms of accuracy and sensitivity across all problems except Turkish banks dataset.     

基于小波神经网络的空调系统传感器故障诊断

详细阐述了小波神经网络(WNN)的原理、结构,并对传统的BP算法进行了改进。以空调系统传感器故障检测问题为目标,提出了基于WNN的故障诊断方法。通过采集天津博物馆中的传感器数据,对训练好的WNN进行了传感器故障诊断能力的验证,对温度传感器的1℃偏差故障、0.05℃/s速率漂移故障、完全故障、与不同方差下的精度等级下降故障进行了仿真,结果表明:这种方法对传感器故障具有很好的诊断效果。     

Robust tuning for machine-directional predictive control of MIMO paper-making processes

This paper solves the controller tuning problem of machine-directional predictive control for multiple-input–multiple-output (MIMO) paper-making processes represented as superposition of first-order-plus-dead-time (FOPDT) components with uncertain model parameters. A user-friendly multi-variable tuning problem is formulated based on user-specified time domain specifications and then simplified based on the structure of the closed-loop system. Based on the simplified tuning problem and a proposed performance evaluation technique, a fast multi-variable tuning technique is developed by ignoring the constraints of the MPC. In addition, a technique to predict the computation time of the tuning algorithm is proposed. The efficiency of the proposed method is verified through Honeywell real time simulator platform with a MIMO paper-making process obtained from real data from an industrial site.