Implementation of Multimodel-Based PID and Intelligent Controller for Simulated and Real-Time Temperature Control of Injection Molding Machine

The proposed real-time multimodel for the injection molding process mainly contributes to the barrel temperature control. Good control of the plastic melt temperature is very important for injection molding in reducing the operator setup time, ensuring product quality, and preventing thermal degradation of the melt. The controllability and set points of the barrel temperature also depend on the precise monitoring and control of the plastic melt temperature. Motivated by the practical temperature control of injection molding, this article proposes a multimodel-based proportional integral derivative (PID) control scheme in real-time and the simulation studies of the PID, fuzzy, and adaptive neuro fuzzy inference system (ANFIS) control schemes. The injection molding process consists of three zones, and the mathematical model for each zone is different. The control output for each zone controller is assigned a weight, based on the computed probability of each model, and the resulting action is the weighted average of the control moves of the individual zone controller.     

基于自适应神经模糊推理系统的刀具磨损监测

为精确地监测高速铣床刀具在加工过程中的刀具磨损量,通过采集高速铣床加工过程中的振动信号、电流信号和噪声信号,经数据预处理与数据融合,建立基于自适应神经模糊推理系统的刀具磨损过程变化模型,实现在高速铣床不停机的前提下对铣床刀具进行状态监控。实验结果显示：针对铣床刀具磨损量的监测平均准确率为95.21％,最大监测准确率为99．74％。这表明文中所采用的方法具有较高的可行性。     

基于模糊神经网络系统的结构主动控制

目的 应用自适应模糊神经网络对多维地震动下结构的振动进行主动控制．方法 用这种自适应模糊神经网络作为主动控制器，以结构的位移和加速度作为输入，计算出主动控制力．结果 将计算的主动控制力输入到结构的动力方程中，结构的位移响应有了较大幅度地减少．同被动控制相比有较大提高．结论 自适应模糊神经网络是一种适用于对结构进行主动控制的智能算法。该控制系统无需引入结构的运动模型和精确参数；对复杂的结构易于建模；同被动控制相比其适应力强，消振迅速而且效果良好．     

基于ANFIS的非线性电机系统的建模

将一种神经—模糊结构—自适应神经模糊推理系统 (简称ANFIS)用于非线性电机系统的建模 ,获得了一个良好的大范围的全局非线性模型 ,同时 ,通过与反向传播网络建模结果的性能对比 ,说明ANFIS在参数收敛速度及建模精度上的优越性。显示出ANFIS是非线性系统的建模、辨识的有力工具     

基于SIMULINK的物料悬浮速度仿真研究

SIMULINK是MATLAB重要的组件之一，它为模拟动态系统提供了交互式程序，允许用户在屏幕上绘制框图来模拟系统。并能够动态地控制该系统。本文基于上述软件工具，设计一个模糊逻辑控制器，并应用于球形物料悬浮速度的仿真计算。从建立系统的仿真模型到仿真结果的图形化处理变得非常简单、直观和方便。     

ANFIS模型在围岩稳定性分类中的应用

将模糊理论和人工神经网络理论相结合,建立了一种自适应神经模糊推理系统(ANFIS),应用于地下工程围岩稳定性分类.并根据收集到的围岩分类资料作为样本来训练和测试网络模型.预测结果表明,该模型能较好地用于地下硐室围岩分类.     

带集中质量智能加肋板振动的自适应模糊控制

带有附加集中质量的加肋板振动控制问题在许多工程中都有重要意义.文章通过基于相互作用的带有附加集中质量和智能加肋梁的正交各向异性单向连续板振动问题的仿真力学模型,对加肋梁采用了ER电流变智能复合材料夹层梁的结构形式,并通过对ER流体施加电场,改变加肋梁结构的刚度和阻尼,从而调整带有附加集中质量加肋连续板的动力响应特性,对这一类结构进行了智能主动控制问题的仿真实验.在仿真试验数据的基础上应用自适应神经-模糊推理控制的方法,分别对激振频率和附加集中质量安装点的位置发生变化时,对带有附加集中质量的这种智能结构的振动问题进行了智能控制方法的仿真研究.仿真结果表明,自适应神经-模糊推理控制的方法能有效地抑制该类结构的振动.     

基于ANFIS模型的年径流预报方法

分析以往年径流预报方法的特点,阐述自适应神经模糊推理系统(adaptive network-based fuzzyinference system,ANFIS),提出年径流预报的ANFIS模型,并将其应用到西北地区某水文站年径流预报中.以MATLAB为工具,依据该地区历年水文资料,对年径流量进行预报.实例结果表明,与改进的ANN模型(最速下降—共轭梯度法、进化单纯形法)相比,本方法计算速度快、泛化能力强、预报精度高,说明ANFIS在年径流预报方面具有良好的适用性.     

AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING

The stock market is a highly complex and dynamic system, and forecasting stock is complicated and difficult. Successful prediction of stock prices may promise attractive benefits; therefore, stock market forecasting is important and of great interest. The economy of Taiwan relies on international trade deeply and the fluctuations of international stock markets impact Taiwan's stock market to certain degree. It is practical to use the fluctuations of other stock markets as forecasting factors for forecasting on the Taiwan stock market. Further, stock market investors usually make short-term decisions based on recent price fluctuations, but most time series models use only the last period of stock price in forecasting. In this article, the proposed model uses the fluctuations of other national stock markets as forecasting factors and employs an expectation equation method whose parameters are optimized by a genetic algorithm (GA) joined with an adaptive network–based fuzzy inference system (ANFIS) model to forecast the Taiwan stock index. To evaluate the forecasting performance, the proposed model is compared with Chen's model and Yu's model. The experimental results indicate that the proposed model is superior to the listing methods (Chen's model and Yu's model) in terms of root mean squared error (RMSE).     

Object-oriented approach applied to ANFIS modeling and control of a distillation column

Neurofuzzy networks are hybrid systems that combine neural networks with fuzzy systems, and the Adaptive Neuro-Fuzzy inference system (ANFIS) is a particular case in which a fuzzy system is implemented in the framework of an adaptive neural network. This neurofuzzy approach represents an effective structure to the modeling of plant dynamics, and the oriented-object programming environments offer an intuitive way to address this task. In this paper the MODELICA object-oriented environment has been applied to the ANFIS modeling and indirect control of the heavy and light product composition in a binary methanol-water distillation column by using the adaptive Levenberg–Marquardt approach. The results obtained demonstrate the potential of the adaptive ANFIS scheme under MODELICA for the dual control of composition both for changes in set points with null stationary error even when disturbances are present.