自适应模糊神经网络在污水处理中的应用

设计了基于神经网络的自适应模糊控制器，利用他对城市污水处理过程中的污泥龄进行控制。该自适应模糊神经网络能从一组操作数据中提取模糊控制规则，改善了污水处理中污泥龄的控制，从而提高了污水厂出水水质。     

基于自组织模糊神经网络的污水处理出水水质预测建模

提出了一种新的自组织模糊神经网络算法,该算法能够基于输入数据自动进行神经网络结构辨识和参数辨识。首先采用一种自组织聚类方法得到神经网络的结构和网络参数初值,然后采用监督学习来优化网络参数。以某污水处理厂的运行数据为对象,应用该自组织模糊神经网络建立了活性污泥污水处理系统出水水质预测模型。仿真结果表明,该模型能够对污水处理系统出水水质进行较好的预测。     

SPR污水处理系统的智能控制

针对SPR污水处理系统工作中所存在的问题,控制出水化学需氧量COD保持恒定,以保证在各种务件下出水的水质,介绍了一种采用混合型模糊控制器控制的SPR污水处理系统的控制原理,并设计了模糊控制器、PI调节器和控制软件.仿真表明控制速度快,超调量小,调节时间短.系统动态性能和静态性能良好,具有较高的鲁棒性和性价比,实用性强.     

基于模糊神经网络预报的抽油机节能控制研究

基于油田多数抽油机轻载运行的现状,提出抽油机"间歇启停"运行的节能控制方案,考虑采油为一复杂过程,选择模糊神经网络(FNN)预报方法给予实现.为了实现这一方案,首先介绍了T-S模糊神经网络的结构,结合抽油过程的特点和研究的需要,对其进行了简化和改进;然后运用采油现场的样本信息和专家知识对FNN进行训练;最后给出了实用的抽油机节能FNN预报算法,将该算法应用在智能抽油机控制器中,取得了满意的节能效果.     

具有神经网络前馈控制器的污水处理模糊控制系统设计

为解决污水处理过程中非线性、大时滞及干扰严重的问题,提出一种将模糊控制器和前馈控制器相接合的控制方法。其中在建立前馈控制器时,提出了使用神经网络建立前馈控制器的新方法。使用这种方法建立前馈控制器无需建立控制系统控制通道和干扰通道的数学模型,也不要求干扰是可以测量的。在MATLAB环境下对控制系统进行仿真,结果表明,将模糊控制和前馈控制结合可以有效克服干扰,提高系统的稳态精度;基于神经网络的前馈控制器可以有效补偿污水处理过程中的干扰,具有实际意义。     

CDMA蜂窝系统中基于模糊神经网络的反向链路功率控制

CDMA是一个干扰受限系统,反向链路功率控制对于克服“远近效应”和增加系统容量是非常重要的.本文提出了一种基于模糊神经网络(FNN)的自适应闭环功率控制算法,该算法动态地调整功率控制增量,使基站接收到的每个用户的发射功率相等.仿真结果表明,由于模糊神经网络能够较好地识别反向链路的时变特性,FNN功率控制算法比传统的固定步长功率控制方法取得了更好的控制性能和更大的系统容量.而且,FNN能够通过神经网络训练自动地调整隶属度函数和模糊规则,从而适合于实现在线系统识别和自适应控制.     

基于模糊神经网络的网络业务量预测研究

利用神经网络(NN)的自学习能力以及模糊逻辑的动态性和及时性等特点,将模糊逻辑和 NN 有机地结合起来,构造出了五层模糊神经网络(FNN),并用训练 NN 的相应学习算法-BP 算法来训练网络。本文将 FNN 用于网络自相似业务预测研究中,并与单纯的 NN 算法相比较。仿真结果表明,FNN 能很好地预测复杂网络业务,与传统的 NN 算法相比,不仅收敛速度快,且得到更好的预测效果。本文为复杂网络业务流量预测研究提供了一种有效途径。     

微波化学技术在污水处理中的应用

介绍了微波技术、微波化学污水处理的原理、微波化学污水处理装置的特点,微波化学技术在焦化污水、养猪场污水、医院污水中的试验情况。试验结果表明：微波系统出水水质指标达到相应的国家排放标准,出水水质良好。     

DSP在污水处理溶解氧控制方面的应用

针对连续循环曝气系统(CCAS)污水处理中溶解氧浓度控制存在的实时性差、溶解氧浓度波动范围偏大、运行费用较高等问题,建立溶解氧DO环、速度环和电流环组成三闭环的系统模式,达到对溶解氧的稳定控制.以TMS320LF2812作为主控芯片,通过对风杌施以FOC的矢量变频调速控制以控制风机的鼓风量;同时对溶解氧施以Smith预估补偿控制,消除对溶解氧控制的滞后,提高系统的鲁棒性.在不同阶段溶解氧参数稳定效果好,出水水质指标明显提高.系统具有良好的动态性能,系统硬件电路简单经济、性价比高,有较高的实用价值.     

其它工艺与设备

0322433A~2/O 污水处理过程出水水质的多元线性回归软测量模型[刊]/冯丽辉//测控技术.—2003,22(6).—11～14(E)为实现污水处理出水水质的预测预报,本文以昆明市污水处理厂的 A~2/O 工艺为背景,针对其出水水质多个参量难以在线测量的问题,提出采用软测量技术,建立了多输入多输出的多元线性顺归软测量模型。参7     

基寸S7-200PLC的模糊神经网络算法设计

随着智能信息技术的发展,模糊神经网络算法广泛应用于工业控制。但该算法尚未应用于PLC。针对这种现状．给出基于S7-200PLC的模糊神经网络算法设计。利用模糊神经网络算法的理论知识,在S7—200的平台上采用梯形图和指令表两种模式编程设置。并利用PLC仿真软件对其仿真,仿真结果达到预期目标。     

On the Equivalence of a Table Lookup (TL) Technique and Fuzzy Neural Network (FNN) With Block Pulse Membership Functions (BPMFs) and Its Application to Water Injection Control of an Automobile

This paper presents an alternative method to design a fuzzy neural network (FNN) using a set of nonoverlapped block pulse membership functions (BMPFs), and this FNN with nonoverlapped BPMFs will be shown to be equivalent to the conventional table lookup (TL) technique. Therefore, the hidden links between TL and FNN techniques are revealed in this paper that provides a methodology to design a TL controller based on the FNN design concept. In order to do so, a new direct formula is first developed to generate the fuzzy rules from the premise part in FNN. This direct formula not only guarantees a one-to-one mapping that maps the fuzzy membership functions onto the fuzzy rules, but also alleviates the coding effort during hardware implementation. It is further elaborated that the FNN with nonoverlapped BPMFs has the advantage of faster online training that requires less computation time, but at the cost of more memory requirement to store the fuzzy rules. The application of this new approach has been applied successfully in the water injection control of a turbo-charged automobile with excellent results.     

信用风险预警的MA—RSFNN模型构建与应用

为了克服模糊神经网络的维数灾难、结构复杂、局部早熟及收敛慢等缺陷,在设计一种模糊神经网络的基础上,将模因算法和粗糙集理论引入模糊神经网络,提出一种模因进化型粗糙模糊神经网络（MA—RSFNN）。新模型借助模因算法的全局搜索能力减少网络陷入局部极值的可能性,同时利用粗糙集知识约简对网络输人数据进行降维消冗,精简输人维度,避免“维数灾难”。实例仿真结果表明MA—RSFNN模型的预测准确性较高,是一类解决金融风险管理中高维复杂问题的有效方法。     

Membership function learning in fuzzy classification

A novel three-layer fuzzy neural network (FNN) is proposed which possesses ihe structure and learning ability of artificial neural networks, and the classification ability of fuzzy algorithms for pattern classification problems. During learning, the proposed FNN learns the membership function of each fuzzy class from training samples and adaptively organizes its hidden layer. The learning and recall times of the FNN are fast. Simulation results are also presented.     

Fuzzy neural network-based real-time self-reaction of mobile robot in unknown environments

In this paper, a hybrid intelligent method including fuzzy inference and neural network is presented for real-time self-reaction of a mobile robot in unknown environments. A neural network with fuzzy inference (fuzzy neural network, FNN) presented can effectively improve the learning speed of the neural network. The method can be used to control a mobile robot based on the present motion situations of the robot in real-time; these situations include the distances in different directions between the obstacles and the robot provided by ultrasonic sensors, the target orientation sensed by a simple optical range-finder and the movement direction of the robot. Simulation results showed that the above method can quickly map the fuzzy relationship between the inputs and the output of the control system of the mobile robot.     

基于模糊神经网络的二相码旁瓣抑制

研究了模糊神经网络在二相码旁瓣抑制中的应用,对网络的学习算法进行了改进,采用梯度下降算法优化规则前件参数,而用最小二乘算法优化规则后件参数.对13位巴克码进行的仿真结果表明,改进的算法具有极快的收敛速度,可获得60 dB以上的输出主副比,提高了雷达的探测性能.     

Comparison of sliding-mode and fuzzy neural network control formotor-toggle servomechanism

A comparative study of sliding-mode control and fuzzy neural network (FNN) control on the motor-toggle servomechanism is presented. The toggle mechanism is driven by a permanent-magnet synchronous servomotor. The rod and crank of the toggle mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the equation of motion. Then, based on the principles of the sliding-mode control, a robust controller is developed to control the position of a slider of the motor-toggle servomechanism. Furthermore, an FNN controller with adaptive learning rates is implemented to control the motor-toggle servomechanism for the comparison of control characteristics. Simulation and experimental results show that both the sliding-mode and FNN controllers provide high-performance dynamic characteristics and are robust with regard to parametric variations and external disturbances. Moreover, the FNN controller can result in small control effort without chattering     

Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.