Pseudoerror-based self-organizing neuro-fuzzy system

The novel concept of pseudoerrors for a self-organizing neuro-fuzzy system (SO-NFS) is proposed for tracking control problem. To demonstrate the proposed approach, an example of motion control of an auto-warehousing crane system is illustrated, which can move back and forth in x,y, and z directions to access and store cargoes. The proposed SO-NFS shows excellent performance in control of the crane system for different loading conditions and varying distances in all directions.     

Control of Shape Memory Alloy Actuators with a Neuro-fuzzy Feedforward Model Element

This paper describes development of a motion controller for Shape Memory Alloy (SMA) actuators using a dynamic model generated by a neuro-fuzzy inference system. Using SMA actuators, it would be possible to design miniature mechanisms for a variety of applications including miniature robots for micro manufacturing. Today SMA is used for valves, latches, and locks, which are automatically activated by heat. However it has not been used as a motion control device due to difficulty in the treatment of its highly nonlinear strain-stress hysteresis characteristic. In this paper, a dynamic model of a SMA actuator is developed using ANFIS, a neuro-fuzzy inference system provided in MATLAB environment. Using neuro-fuzzy logic, the system identification of the dynamic system is performed by observing the change of state variables (displacement and velocity) responding to a known input (input voltage to the current amplifier for the SMA actuator). Then, using the dynamic model, the estimated input voltage required to follow a desired trajectory is calculated in an open-loop manner. The actual input voltage supplied to the current amplifier is the sum of this open-loop input voltage and an input voltage calculated from an ordinary PD control scheme. This neuro-fuzzy logic-based control scheme is a very generalized scheme that can be used for a variety of SMA actuators. Experimental results are provided to demonstrate the potential for this type of controller to control the motion of the SMA actuator.     

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.     

A novel approach for ANFIS modelling based on full factorial design

Adaptive neural network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input–output data pairs of the system under consideration. The size of the input–output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper, we have proposed an ANFIS based system modelling where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data and a data set collected from a thermal power plant of the North Eastern Electric Power Corporation (NEEPCO) Limited. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced and thereby computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model.     

An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices

The close price prediction model of the Zagreb Stock Exchange Crobex® index is presented in this paper. For the input/output data plan modeling the Crobex® index close price historical data are retrieved from the Zagreb Stock Exchange official internet pages. The prediction model is created in the way that for each of 5 days in advance it predicts the Crobex® close price. The prediction model is generated based on the input/output data plan by means of the adaptive neuro-fuzzy inference system method, representing the fuzzy inference system. It is of the essence to point out that for each day a separate fuzzy inference system is created by means of the adaptive neuro-fuzzy inference system method based on the same set of input/output data, the only difference being that for every separate fuzzy inference system different subsets for training and checking are used so that input variables are differently created. The input/output data set represents the historical data of the Crobex® index close price from 4 November 2010 to 24 January 2012 and the Crobex® index close price is predicted for the subsequent 5 days, the first day of prediction being 25 January 2012. After that the above mentioned input/output data set is shifted 5 days in advance and the Crobex® index close price is predicted in advance for the next 5 days starting with the last day of the input/output data set. In that way the Crobex® index close prices are predicted until 19 October 2012 based on the Crobex® index close price historical data. At the end of the paper qualitative and quantitative estimates are presented for the given approach of predicting the Crobex® index close price showing that the approach is useful for predicting within its limits.     

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 fusion algorithm based on ANFIS for radar/infrared system

In order to improve tracking ability, an adaptive fusion algorithm based on adaptive neuro-fuzzy inference system (ANFIS) for radar/infrared system is proposed, which combines the merits of fuzzy logic and neural network. Fuzzy adaptive fusion algorithm is a powerful tool to make the actual value of the residual covariance consistent with its theoretical value. To overcome the defect of the dependence on the knowledge of the process and measurement noise statistics of Kalman filter, neural network is introduced, which has the ability to learn from examples and extract the statistical properties of the examples during the training sessions. The fusion system mainly consists of Kalman filters, ANFIS sensor confidence estimators (ASCEs) based on contextual information (CI) theory, knowledge base (KB) and track-to-track fusion algorithms. Experimental data are implemented to train ASCEs to obtain sensor confidence degree. Simulation results show that the algorithm can effectively adjust the system to adapt contextual changes and has strong fusion capability in resisting uncertain information.     

A method for design of a hybrid neuro-fuzzy control system based onbehavior modeling

It is known that control signals from a fuzzy logic controller are determined by a response behavior of a controlled object rather than its analytical models. That implies that the fuzzy controller could yield a similar control result for a set of plants with a similar dynamic behavior. This idea lends to modeling of a plant with unknown structure by defining several types of dynamic behaviors. On the basis of dynamic behavior classification, a new method is presented for the design of a neuro-fuzzy control system in two steps: 1) we model a plant with unknown structure by choosing a set of simplified systems with equivalent behavior as “templates” to optimize their fuzzy controllers off-line; and 2) we use an algorithm for system identification to perceive dynamic behavior and a neural network to adapt fuzzy logic controllers by matching the “templates” online. The main advantage of this method is that convergence problem can be avoided during adaptation process. Finally, the proposed method is used to design neuro-fuzzy controllers for a two-link manipulator     

Derivation of a parameter stabilizing training criterion for adaptive neuro-fuzzy inference systems in motion control

This paper presents a novel training algorithm for adaptive neuro-fuzzy inference systems. The algorithm combines the error back-propagation algorithm with the variable structure systems approach. Expressing the parameter update rule as a dynamic system in continuous time and applying sliding mode control (SMC) methodology to the dynamic model of the gradient based training procedure results in the parameter stabilizing part of training algorithm. The proposed combination therefore exhibits a degree of robustness to the unmodelled multivariable internal dynamics of the gradient-based training algorithm. With conventional training procedures, the excitation of this dynamics during a training cycle can lead to instability, which may be difficult to alleviate owing to the multidimensionality of the solution space and the ambiguities concerning the environmental conditions. This paper shows that a neuro-fuzzy model can be trained such that the adjustable parameter values are forced to settle down (parameter stabilization) while minimizing an appropriate cost function (cost optimization), which is based on state tracking performance. In the application example, trajectory control of a two degrees of freedom direct drive robotic manipulator is considered. As the controller, an adaptive neuro-fuzzy inference mechanism is used and, in the parameter tuning, the proposed algorithm is utilized.     

Simplifying a neuro-fuzzy model

Neuro-fuzzy modeling allows a fuzzy system to be refined by neural training, thus avoiding lenghty trial-and-error phases in defining both membership functions and inference rules. An approach to obtain simple neuro-fuzzy models is proposed, which reduces the number of rules by means of a systematic procedure that consists in successively removing a rule and updating the remaining rules in such a way that the overall input-output behavior is kept approximately unchanged over the entire training set. A formulation of the proper update is described and a criterion for choosing the rules to be removed is also provided. Initial experimental results show the effectiveness of the proposed method in reducing the complexity of a neuro-fuzzy system by using its input-output data.     

Approximation of dynamic systems using recurrent neuro-fuzzy techniques

Fuzzy systems, neural networks and its combination in neuro-fuzzy systems are already well established in data analysis and system control. Especially, neuro-fuzzy systems are well suited for the development of interactive data analysis tools, which enable the creation of rule-based knowledge from data and the introduction of a-priori knowledge into the process of data analysis. In this article an architecture is presented that was designed to learn and optimize a hierarchical fuzzy rule base with feedback connections using a genetic algorithm for rule base structure learning and a gradient descent method to optimize the fuzzy sets of the learned rule base. Since this architecture is able to store information of prior system states, the model is especially suited for the analysis of dynamic systems.     

一般严格反馈型非线性系统的自适应控制

研究一般严格反馈型非线性系统的控制问题．假设系统的对象模型、状态均未知,只有输出是可测的．应用自适应模糊神经推断系统辨识对象模型,状态观测器设计为Luenberger型,控制器由反步控制、变结构控制和3层神经网络直接控制综合而成．理论分析和仿真研究都说明此方案能够有效地控制只有输出可测的一般严格反馈型非线性系统．     

Hierarchical wavelet packet fuzzy inference system for pattern classification and system identification

This study presents a hierarchical Takagi–Sugeno–Kang type fuzzy system called hierarchical wavelet packet fuzzy inference system. In the proposed method, wavelet packet transform is applied on the input data to produce approximation and detail sub-bands of the input data and the output is used as the input vector of the proposed network. This network uses a hierarchical structure same as wavelet packet decomposition tree, in which adaptive network-based fuzzy inference system is used as sub-model. Also, gradient descent algorithm is chosen for training the parameters of antecedent and conclusion parts of the sub-models. In order to evaluate the capability of the proposed method, its applications in pattern classification, system identification and time-series prediction have been studied. The results show that the proposed method performs better than the other conventional models.     

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 neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems

This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based approach to prevent developing of fault as soon as possible. By comparison of plants and neuro-fuzzy estimator outputs in the presence of noise, residual signal is generated and compared with a predefined threshold, the fault can be detected. To diagnose the type, size, time and fault conditions, are used analytical approach and neural network for tracking fault developing online. The neuro-fuzzy nets are compared with some other identification methods in application of power plant gas turbine. Faults are considered in two forms, step, and ramp shape. This work was implemented with real data from gas turbine of Kazeroun (Iran) power plant (Mitsubishi unit) and result is presented to demonstrate the benefits of the proposed method.     

图像脉冲噪声检测

提出了一个包含两个自适应神经模糊推理系统和一个后处理块的网络,该网络可用于灰度图像脉冲噪声检测。网络中每个自适应神经模糊推理系统都是一个四输入单输出一阶Sugeno模糊推理系统。所提出的脉冲噪声检测方法分两步进行：对该网络进行优化训练,确定其参数;用优化后的网络对被椒盐脉冲噪声污染的图像进行噪声检测。实验结果表明,与其他传统检测方法相比,所提出的方法,更能有效检测出图像中椒盐脉冲噪声。     

A new feature selection from lidar data and digital aerial images acquired for an urban/c environment using an ANFIS-based classification and a fuzzy rough set method

In this article, a new fuzzy rough set (FRS) method was proposed for extracting rules from an adaptive neuro-fuzzy inference system (ANFIS)-based classification procedure in order to select the optimum features. The proposed methodology was used to classify lidar data and digital aerial images acquired for an urban environment to detect four classes, including trees, buildings, roads, and natural grounds. In this regard, 16 potentially primary features were produced for classification using the lidar data and the digital aerial images. The training and checking inputs of the proposed ANFIS were collected from the generated features for further training and evaluation processes. Also, the fuzzy c-mean clustering algorithm was used to initialize the fuzzy inference system of the proposed ANFIS-based classification method. By considering all states of fuzzy rules for each training input, the fuzzy rule with the maximum firing value was selected. Accordingly, these fuzzy rules were used as the inputs of the Rough Set Theory. Accordingly, the optimum features were acquired by the basic minimal covering algorithm as the rule induction method. To validate our proposed methodology, the procedure of classification was repeated by the achieved optimum features. The results showed that the classification using the optimum features has reached better overall accuracy than those achieved by using the 16 potentially primary features. Also, comparing the results of our proposed methodology with the other well-known genetic-algorithm-based feature selection methods indicated the significance of the proposed FRS method to select optimum features with high accuracy in a short running time.     

基于ANFIS的微波炉温度控制

针对微波炉温度对象的不确定性，提出了用自适应神经模糊推理系统(ANFIS)对微波炉温度进行自适应控制的自适应神经模糊控制器。通过对ANFIS的训练及检验，结果表明，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。     

Predicting injection profiles using ANFIS

Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields’ performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptive-network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based FIS, named ANFIS-SUB. We compare the performance of resultant FIS and study the effect of parameters. A real-world injection profile data set from the Daqing Oilfield, China is used. FIS are generated and tested using training and testing data from that data set. The impact of data quality on the performance of FIS is also studied. Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems. ANFIS-GRID outperforms ANFIS-SUB due to its simplicity in parameter selection and its fitness in the target problem.     

Eliciting transparent fuzzy model using differential evolution

In this paper a new technique for eliciting a fuzzy inference system (FIS) from data for nonlinear systems is proposed. The strategy is conducted in two phases: in the first one, subtractive clustering is applied in order to extract a set of fuzzy rules, in the second phase, the generated fuzzy rule base is refined and redundant rules are removed on the basis of an interpretability measure. Finally the centers and widths of the Membership Functions (MFs) are tuned by means differential evolution. Case studies are presented to illustrate the efficiency and accuracy of the proposed approach. The results obtained are compared and contrasted with those obtained from a conventionally neuro-fuzzy technique and the superiority of the proposed approach is highlighted.