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Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro–fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an mth-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors. [ABSTRACT FROM AUTHOR]

Optimizing the traffic signal control has an essential impact on intersections efficiency in urban transportation. This paper presents a two-stage method for intersection signal timing control. First, the traffic volume is predicted using a neuro-fuzzy network called Adaptive neuro-fuzzy inference system (ANFIS). The inputs of this network include two-dimensional, hourly and daily, traffic volume correlations. In the second stage, appropriate signal cycle and optimized timing of each phase of the signal are estimated using a combination of Self Organizing and Hopfield neural networks. The energy function of the Hopfield network is based on a traffic model derived by queuing analysis. The performance of the proposed method has been evaluated for real data. The two-dimensional correlation presents superior performance compared to hourly traffic correlation. The evaluation of proposed overall method shows considerable intersection throughput improvement comparing to the results taken form Synchro software. [ABSTRACT FROM AUTHOR]

Multiscale neurofuzzy modeling combines the multiresolution property of the wavelet transform with the regression ability of neurofuzzy systems. A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in detail and approximation. A neurofuzzy system is then trained on each of the relevant resolution scales (i.e. those scales where significant events are detected); and individual wavelet forecasts are recombined to form the overall forecast. The neurofuzzy models developed in this paper are based on Mamdani and Takagi–Sugeno–Kang approaches to the problem of fuzzy modeling based on the strategy knowledge expressed by the input-output data. Within these approaches, the proposed Neural-Fuzzy Inference System (NFIS) provides several methods that represent different alternatives in the fuzzy modeling process and how they can be integrated with the learning power of neural networks. Simulation results carried out on a forecasting problem associated with stock market, are included to demonstrate the potential of the proposed forecasting scheme. [ABSTRACT FROM AUTHOR]

This paper reports the application of an adaptive neuro-fuzzy inference system (ANFIS) to model the experimental results of heat transfer in an air-cooled heat exchanger equipped with classic twisted tape inserts. The aim is to consider the effects of the twist ratio of classic inserts and Reynolds number variation on average heat transfer in the air cooler. The training data for optimizing the ANFIS structure are based on available experimental data. A hybrid learning algorithm consisting of the gradient descends method and the least-squares method is used for ANFIS training. The proposed ANFIS was developed using MATLAB functions. For the best ANFIS structure obtained in this study, the maximum errors of the training and test data were found to be 0.111% and 2.378%, respectively. Also, the mean relative errors of the training and test data were found to be 0.011% and 1.316%, respectively. The obtained results showed that ANFIS can be used to predict experimental results precisely. [ABSTRACT FROM PUBLISHER]

This paper presents two hybrid neural networks derived from fuzzy neural networks (FNN): wavelet fuzzy neural network (WFNN) using the fuzzified wavelet features as the inputs to FNN and fuzzy neural network (FNCI) employing the Choquet integral as the outputs of FNN. The learning through FNCI is simplified by the use of q-measure and the speed of convergence of the parameters is increased by reinforced learning. The underlying fuzzy models of these hybrid networks are a modified form of fuzzy rules of Takagi-Sugeno model. The number of fuzzy rules is found from a fuzzy curve corresponding to each input-output by counting the total number of peaks and troughs in the curve. The models can forecast hourly load with a lead time of 1 h as they deal with short-term load forecasting. The results of the two hybrid networks using Indian utility data are compared with ANFIS and other conventional methods. The performance of the proposed WFNN is found superior to all the other compared methods. [ABSTRACT FROM AUTHOR]

Continuous Stirred Tank Reactor System (CSTR) is a typical chemical reactor system with complex nonlinear dynamic characteristics. There has been considerable interest in its state estimation and real time control based on mathematical modelling. However, the lack of understanding of the dynamics of the process, the highly sensitive and nonlinear behaviour of the reactor, has made difficult to develop the precise mathematical modelling of the system. An efficient control of the product concentration in CSTR can be achieved only through accurate model. In this paper, attempts are made to alleviate the modelling difficulties using "Artificial Intelligence" (AI) techniques such as neural, fuzzy and neuro-fuzzy. Simulation results demonstrate the effectiveness of Artificial Intelligence modelling techniques. The performance comparison of different modelling techniques has been given in terms of root mean square error. [ABSTRACT FROM AUTHOR]

This paper develops a representation of multi‐model based controllers by using artificial intelligence typical structures. These structures will be neural networks, genetic algorithms and fuzzy logic. The interpretation of multimodel controllers in an artificial intelligence frame will allow the application of each specific technique to the design of improved multimodel based controllers. The obtained artificial intelligence based multimodel controllers are compared with classical single model based ones. It is shown through simulation examples that a transient response improvement can be achieved by using multiestimation based techniques. Furthermore, a method for synthesizing multimodel based neural network controllers from already designed single model based ones is presented. The proposed methodology allows to extend the existing single model based neural controllers to multimodel based ones, extending the applicability of this kind of techniques to a more general type of controllers. Also, some applications of genetic algorithms and fuzzy logic to multimodel controller design are proposed. Thus, the mutation operation from genetic algorithms inspires a robustness test which consists of a random modification of the estimates which is used to select the estimates leading to the better identification performance towards parameterizing online the adaptive controller. Such a test is useful for plants operating in a noisy environment. The proposed robustness test improves the selection of the plant model used to parameterize the adaptive controller in comparison to classical multimodel schemes where the controller parameterization choice is basically taken based on the identification accuracy of each model. Moreover, the fuzzy logic approach suggests new ideas to the design of multiestimation structures which can be applied to a broad variety of adaptive controllers such as robotic manipulator controller design. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for creating rules of a fuzzy logic system using a neuro-fuzzy approach. The proposal is based on the results of Juang and Tsao who use a Fuzzy Neural Network (FNN) to generate rules and fuzzy sets from input data. A time series example is solved using our proposal, which is tested using statistical analysis of the residuals of the model. [ABSTRACT FROM AUTHOR]

Scale is highly detrimental to surface quality for tinplate products. There are a large number of process variables at a typical hot mill and principal component analysis is a well-known technique for reducing the number of process variables. This paper estimates the principal components associated with the hot mill process variables and puts these through an Adaptive Neuro Fuzzy Inference System (ANFIS) to find those hot mill running conditions that will minimise the amount of scale observed on the bottom of the rolled strip. It was found that the variation observed in all the hot mill process variables could be captured through the use of just six principal components, and that using just three of these in an ANFIS was sufficient to identify those operating conditions leading to coils being produced with a consistently low scale count. Specifically, it was found that the best operating conditions for the hot mill were when the first component was lower than -0.098 the second lower than 0.8058 and the third higher than -0.482. These ranges in turn corresponded to certain hot mill temperatures that depended to some extent on the base chemistry of the incoming slab. [ABSTRACT FROM AUTHOR]

Abstract: Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques. [Copyright &y& Elsevier]

Abstract: A magnetic tube recovery ratio (MTRR) is an important index in mineral processing, though it cannot be measured online. Real-time control for this product index is impossible. In this paper a new soft sensing method is proposed, which uses fuzzy system and neural network techniques. The contributions of our soft sensing method are that a fuzzy mechanism model for MTRR is used, which is obtained from data clustering. We do not update the fuzzy model, but use a neural compensator to improve the modeling accuracy, where the training algorithm for the neural network stable. This soft sensing method has been successfully applied in a metal company in China. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a neural network (NN)-based multi-agent classifier system (MACS) using the trust, negotiation, and communication (TNC) reasoning model. The main contribution of this work is that a novel trust measurement method, based on the recognition and rejection rates, is proposed. Besides, an auctioning procedure, based on the sealed bid, first price method, is adapted for the negotiation phase. Two agent teams are formed; each consists of three NN learning agents. The first is a fuzzy min–max (FMM) NN agent team and the second is a fuzzy ARTMAP (FAM) NN agent team. Modifications to the FMM and FAM models are also proposed so that they can be used for trust measurement in the TNC model. To assess the effectiveness of the proposed model and the bond (based on trust), five benchmark data sets are tested. The results compare favorably with those from a number of classification methods published in the literature. [Copyright &y& Elsevier]

Abstract: Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper we propose a hybridization of intelligent techniques such as ANNs, fuzzy systems and evolutionary algorithms, so that the final hybrid ARIMA–ANN model could outperform the prediction accuracy of those models when used separately. More specifically, we propose the use of fuzzy rules to elicit the order of the ARMA or ARIMA model, without the intervention of a human expert, and the use of a hybrid ARIMA–ANN model that combines the advantages of the easy-to-use and relatively easy-to-tune ARIMA models, and the computational power of ANNs. [Copyright &y& Elsevier]

Abstract: The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. This study was designed: (a) to investigate a hybrid methodology that combines ANN and ARMA models; (b) to resolve one of the most important problems in time series using ARMA structure and Box–Jenkins methodology: the identification of the model. In this paper, we present a new procedure to predict time series using paradigms such as: fuzzy systems, neural networks and evolutionary algorithms. Our goal is to obtain an expert system based on paradigms of artificial intelligence, so that the linear model can be identified automatically, without the need of human expert participation. The obtained linear model will be combined with ANN, making up an hybrid system that could outperform the forecasting result. [Copyright &y& Elsevier]

Skin color segmentation by a block histogram-based neural fuzzy network is proposed in this paper. The Hue-Saturation (HS) color model is used. Color information is represented by a block histogram in an HS space image. Several non-uniform quantization approaches on HS space are proposed to represent histogram information as accurately as possible. The neural fuzzy network used is the self-constructing neural fuzzy inference network (SONFIN). Block histogram information from images under different environments is used to train SONFIN to make the method as robust as possible. Experiments on skin color segmentation are performed to verify the performance of the proposed method. For comparison, three other segmentation methods, including principal component transformation (PCT), histogram-based skin classifier (HSC), and mixture of Gaussian classifier (MGC) are applied to the same problem. Comparisons show that the proposed approach achieves the best segmentation results. In addition, the proposed non-uniform HS partition approach also improves segmentation performance. [ABSTRACT FROM AUTHOR]