An IV-QR Algorithm for Neuro-Fuzzy Multivariable Online Identification

In this paper, a new algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamic systems, more specifically applied to consequent parameters estimation of the neuro-fuzzy inference system, is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) recurrent neuro-fuzzy structure from input-output samples of a multivariable nonlinear dynamic system in a noisy environment. The adaptive weighted instrumental variable (WIV) algorithm by QR factorization based on the numerically robust orthogonal Householder transformation is developed to modify the consequent parameters of the TS multivariable neuro-fuzzy network     

A modified gradient-based neuro-fuzzy learning algorithm and its convergence

Neuro-fuzzy approach is known to provide an adaptive method to generate or tune fuzzy rules for fuzzy systems. In this paper, a modified gradient-based neuro-fuzzy learning algorithm is proposed for zero-order Takagi-Sugeno inference systems. This modified algorithm, compared with conventional gradient-based neuro-fuzzy learning algorithm, reduces the cost of calculating the gradient of the error function and improves the learning efficiency. Some weak and strong convergence results for this algorithm are proved, indicating that the gradient of the error function goes to zero and the fuzzy parameter sequence goes to a fixed value, respectively. A constant learning rate is used. Some conditions for the constant learning rate to guarantee the convergence are specified. Numerical examples are provided to support the theoretical findings.     

Self-organizing neuro-fuzzy system for control of unknown plants

A cluster-based self-organizing neuro-fuzzy system (SO-NFS) is proposed for control of unknown plants. The neuro-fuzzy system can learn its knowledge base from input-output training data. A plant model is not required for training, that is, the plant is unknown to the SO-NFS. Using new data types, the vectors and matrices, a construction theory is developed for the organization process and the inference activities of the cluster-based SO-NFS. With the construction theory, a compact equation for describing the relation between the input base variables and inference results is established. This equation not only gives the inference relation between inputs and outputs but also specifies the linguistic meanings in the process. New pseudo-error learning control is proposed for closed-loop control applications. Using a cluster-based algorithm, the neuro-fuzzy system in its genesis can be generated by the stimulation of input/output training data to have its initial control policy (IF-THEN rules) for application. With the well-known random optimization method, the generated neuro-fuzzy system can learn its data base for specific applications. The proposed approach can be applied on control of unknown plants, and can levitate the curse of dimensionality in traditional fuzzy systems. Two examples are demonstrated.     

Efficient Hardware/Software Implementation of an Adaptive Neuro-Fuzzy System

This paper describes the development of efficient hardware/software (HW/SW) neuro-fuzzy systems. The model used in this work consists of an adaptive neuro-fuzzy inference system modified for efficient HW/SW implementation. The design of two different on-chip approaches are presented: a high-performance parallel architecture for offline training and a pipelined architecture suitable for online parameter adaptation. Details of important aspects concerning the design of HW/SW solutions are given. The proposed architectures have been implemented using a system-on-a-programmable-chip. The device contains an embedded-processor core and a large field programmable gate array (FPGA). The processor provides flexibility and high precision to implement the learning algorithms, while the FPGA allows the development of high-speed inference architectures for real-time embedded applications.     

基于ANFIS的水净化过程控制仿真

针对水净化过程的不确定性,提出了将自适应神经模糊推理系统(ANFIS)应用于水净化过程。采用相应的自适应控制方法,完全摆脱了原始的依靠工人经验的传统控制方法。通过对ANFIS的训练及检验,并对水净化过程进行仿真研究表明,该自适应神经模糊控制器具有较高的控制精度,控制效果较好。采用自适应神经模糊控制器处理后的污水,可以满足更高的水质标准,表现出了自适应神经模糊推理系统在现代工业中应用的长处。     

An adaptive recurrent fuzzy system for nonlinear identification

This paper describes the architecture and training procedure of a recurrent fuzzy system (RFS). The RFS is composed of a fuzzy inference system (FIS) and a delayed feedback connection. The recurrent property comes from feeding the FIS output back to the FIS input via an adjustable feedback parameter. Both the on-line and off-line training procedures based on the backpropagation-through-time (BPTT) algorithm have been investigated. The adjoint model of the RFS is obtained and used to compute the gradients. It is shown that the off-line training is insufficient to adapt to changes in system dynamics. So, an on-line training procedure is derived. In this procedure, a first in first out stack is used to store a certain history of the input–output data to perform a truncated BPTT algorithm. A quasi-Newton optimization method with a line search algorithm is used to adjust the RFS parameters. The performance of the developed RFS is demonstrated by applying to the identification of nonlinear dynamic systems. The simulation studies show that the proposed identification model has the ability to learn dynamics of highly nonlinear systems and compensate system uncertainties. The results are promising for the further application in the area of control and modeling.     

An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle

This study presents an adaptive neuro-fuzzy sliding-mode-based genetic algorithm (ANFSGA) control system for a remotely operated vehicle (ROV) with four degrees of freedom (DOF)s. In many applications, ROVs will need to be capable of maneuvering to any given point, following object, and to be controllable from the surface. Therefore, an ANFSGA control system is introduced for tracking control of the ROV to achieve a high precision position control. Since the dynamic of ROVs are highly nonlinear and time varying, an ANFSGA control system is investigated according to direction-based genetic algorithm (GA) with the spirit of sliding mode control and adaptive neuro-fuzzy sliding mode (ANFS) based evolutionary procedure. In this way, on-line learning ability is employed to deal with the parametric uncertainty and disturbance by adjusting the ANFS inference parameters. In this proposed controller a GA control system is utilized to be the major controller, and stability can be indirectly insured by the concept of sliding mode control system without strict constraints and detailed system knowledge.     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

Pressure–volume–temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson–Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.     

Prediction of ground motion parameters using randomized ANFIS (RANFIS)

In this paper, a novel neuro-fuzzy learning machine called randomized adaptive neuro-fuzzy inference system (RANFIS) is proposed for predicting the parameters of ground motion associated with seismic signals. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The three time domain ground motion parameters which are predicted by the model are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using the database released by PEER (Pacific Earthquake Engineering Research Center). Each ground motion parameter is related to mainly to four seismic parameters, namely earthquake magnitude, faulting mechanism, source to site distance and average soil shear wave velocity. The experimental results validate the improved performance of the machine, with lesser computation time compared to prior studies.     

Soft computing-based expert systems to predict energy consumption and stability margin in turning gaits of six-legged robots

Turning gaits are the most general and very important ones for omni-directional walking of a six-legged robot. Soft computing-based expert systems have been developed in the present work to predict specific energy consumption and stability margin of turning gait of a six-legged robot. Besides back-propagation neural network, three approaches based on adaptive neuro-fuzzy inference system have been developed and their performances are compared with each other. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference systems are found to perform better than other approaches. This could be due to a more exhaustive search conducted by the genetic algorithm in place of back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs.     

基于自适应神经模糊推理系统的噪声消除方法

针对信号处理领域噪声消除的实际问题,提出了一种基于模糊推理的自适应神经网络控制方法.通过自适应神经模糊推理系统(ANFIS)对非线性系统的结构和参数进行辨识与自学习,采用混合学习算法,对前向参数和结论参数分别辨识,在提高精度的同时可加快训练收敛的速度,使控制系统具有良好动静态性和鲁棒性,实现了消除通信系统中噪声的目标,最后对基于ANFIS的噪声消除系统进行了建模和仿真,并与自适应神经网络滤波方法的结果对比,其结果证明了该方法的有效性.     

An Adaptive Neuro-fuzzy Inference System for Robot Handling Fabrics with Curved Edges towards Sewing

This paper presents the design of a neuro-fuzzy visual servoing controller for robot guiding fabrics with curved edges towards sewing. Fabrics comprising real cloths consist of curved edges of arbitrary curvatures that can not be standardized. To overcome this difficulty, the idea is to train the robot sewing system and to apply this methodology in a real-time operation environment. The proposed approach for robot sewing is based on visual servoing and a learning technique that combines neural networks and fuzzy logic. A novel genetic-oriented clustering method is used to construct the initial FIS models and then, adaptive neuro-fuzzy inference systems allow tuning them so that it is possible to obtain better estimates. Extensive experiments were carried out in order to build data sets using fabrics with curved edges of various curvatures. The proposed model is validated using fabrics that had not been included in the training process and the results demonstrate that the proposed approach is efficient and effective for robot guiding fabrics with arbitrary curved edges towards sewing.     

基于Q-学习的模糊神经网络控制器

神经模糊系统在机器人的智能控制中具有巨大的应用潜力,但已有的系统构造方法几乎都面临着样本资源匮乏这一巨大困难。为克服传统系统构造方法可能因样本获取困难而引起的“维数灾难”等问题,该文在模糊神经网络中引入了Q-学习机制,提出了一种基于Q-学习的模糊神经网络模型,从而赋予神经模糊系统自学习能力。文章最后给出了其在菅野模糊小车控制中的仿真结果。实验表明,在神经模糊系统中融入智能学习机制Q-学习是行之有效的;它可以被用来实现机器人智能行为的自学习。值得一提的是,该文的仿真实验在真实系统上同样是容易实现的,只要系统能提供作为评价信号的传感信息即可。     

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton–Jacobi–Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.     

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.     

A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines.

This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.     

考虑柔性关节的机械臂自适应运动控制策略研究

具有柔性关节的轻型机械臂因其自重轻、响应迅速、操作灵活等优点,取得了广泛应用;针对具有柔性关节的机械臂系统的关节空间轨迹跟踪控制系统动力学参数不精确的问题,提出一种结合滑模变结构设计的自适应控制器算法;通过自适应控制的思想对系统动力学参数进行在线辨识,并采用Lyapunov方法证明了闭环系统的稳定性;仿真结果表明,该控制策略保证了机械臂系统对期望轨迹的快速跟踪,具有良好的跟踪精度,系统具有稳定性。     

Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm

A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an incremental learning algorithm to update the parameters of the neuro-fuzzy controller. In this way the latter is able to gradually replace the conventional controller from the control of the system. The proposed new learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters, leading the learning error toward zero. In the simulations and in the experimental studies, it has been tested on the control of antilock breaking system model and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.     

基于未建模动态补偿的非线性自适应切换控制方法

针对一类不确定的离散时间零动态不稳定的单输入-单输出(Single-input single-output, SISO)非线性系统,提出了一种基于未建模动态补偿的非线性控制器. 采用自适应神经模糊推理系统(Adaptive-network-based fuzzy inference system, ANFIS)和一一映射相结合的方法估计未建模动态.在此基础上,提出了由线性自 适应控制器、非线性自适应控制器以及切换机制组成的自适应切换控制方法.该方法通过对上述两种控制器的切换, 保证闭环系统输入输出信号有界的同时,改善系统性能.本文将要求未建模动态全局有界的条件放宽为线性增长, 建立了所提自适应控制方法的稳定性和收敛性分析.通过仿真比较和水箱的液位控制实验,验证了所提方法的有效性.