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Fuzzy Neural Network (fuzzy + neural_network)
Selected AbstractsSensory Modeling of Coffee with a Fuzzy Neural NetworkJOURNAL OF FOOD SCIENCE, Issue 1 2002O. Tominaga ABSTRACT: Models were constructed to predict sensory evaluation scores from the blending ratio of coffee beans. Twenty-two blended coffees were prepared from 3 representative beans and were evaluated with respect to 10 sensory attributes by 5 coffee cup-tasters and by models constructed using the response surface method (RSM), multiple regression analysis (MRA), and a fuzzy neural network (FNN). The RSM and MRA models showed good correlations for some sensory attributes, but lacked sufficient overall accuracy. The FNN model exhibited high correlations for all attributes, clearly demonstrated the relationships between blending ratio and flavor characteristics, and was accurate enough for practical use. FNN, thus, constitutes a powerful tool for accelerating product development. [source] PREDICTING THE IMPACT OF ANTICIPATORY ACTION ON U.S. STOCK MARKET,AN EVENT STUDY USING ANFIS (A NEURAL FUZZY MODEL)COMPUTATIONAL INTELLIGENCE, Issue 2 2007P. Cheng In this study, the adaptive neural fuzzy inference system (ANFIS), a hybrid fuzzy neural network, is adopted to predict the actions of the investors (when and whether they buy or sell) in a stock market in anticipation of an event,changes in interest rate, announcement of its earnings by a major corporation in the industry, or the outcome of a political election for example. Generally, the model is relatively more successful in predicting when the investors take actions than what actions they take and the extent of their activities. The findings do demonstrate the learning and predicting potential of the ANFIS model in financial applications, but at the same time, suggest that some of the market behaviors are too complex to be predictable. [source] Adaptive TS-FNN control for a class of uncertain multi-time-delay systems: The exponentially stable sliding mode-based approachINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2009Tung-Sheng Chiang Abstract This paper presents an adaptive Takagi,Sugeno fuzzy neural network (TS-FNN) control for a class of multiple time-delay uncertain nonlinear systems. First, we develop a sliding surface guaranteed to achieve exponential stability while considering mismatched uncertainty and unknown delays. This exponential stability result based on a novel Lyapunov,Krasovskii method is an improvement when compared with traditional schemes where only asymptotic stability is achieved. The stability analysis is transformed into a linear matrix inequalities problem independent of time delays. Then, a sliding mode control-based TS-FNN control scheme is proposed to achieve asymptotic stability for the controlled system. Since the TS-FNN combines TS fuzzy rules and a neural network structure, fewer numbers of fuzzy rules and tuning parameters are used compared with the traditional pure TS fuzzy approach. Moreover, all the fuzzy membership functions are tuned on-line even in the presence of input uncertainty. Finally, simulation results show the control performance of the proposed scheme. Copyright © 2008 John Wiley & Sons, Ltd. [source] Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoringINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2002F. Hoffmann In this paper, we evaluate and contrast two types of fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimization and boosting for learning fuzzy classification rules. The second classifier is a fuzzy neural network that employs a fuzzy variant of the classic backpropagation learning algorithm. The experiments are carried out on a real life credit scoring data set. It is shown that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5(rules) decision tree(rules) induction algorithm. However, the better performance of the genetic fuzzy classifier is offset by the fact that it infers approximate fuzzy rules which are less comprehensible for humans than the descriptive fuzzy rules inferred by the neurofuzzy classifier. © 2002 Wiley Periodicals, Inc. [source] A New Navigation Method for an Automatic Guided VehicleJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 3 2004Chen Wuwei This paper presents a new navigation method for an automatic guided vehicle (AGV). This method utilizes a new navigation and control scheme based on searching points on an arc. Safety measure indices are defined and are generated from the output of a fuzzy neural network which define the actions the AGV is to take when in the presence of obstacles. The proposed algorithm integrates several functions required for automatic guided vehicle navigation and tracking control and it exhibits satisfactory performance when maneuvering in complex environments. The automatic guided vehicle with this navigation control system not only can quickly process environmental information, but also can efficiently avoid dynamic or static obstacles, and reach targets safely and reliably. Extensive simulation and experimental results demonstrate the effectiveness and correct behavior of this scheme. © 2004 Wiley Periodicals, Inc. [source] Sensory Modeling of Coffee with a Fuzzy Neural NetworkJOURNAL OF FOOD SCIENCE, Issue 1 2002O. Tominaga ABSTRACT: Models were constructed to predict sensory evaluation scores from the blending ratio of coffee beans. Twenty-two blended coffees were prepared from 3 representative beans and were evaluated with respect to 10 sensory attributes by 5 coffee cup-tasters and by models constructed using the response surface method (RSM), multiple regression analysis (MRA), and a fuzzy neural network (FNN). The RSM and MRA models showed good correlations for some sensory attributes, but lacked sufficient overall accuracy. The FNN model exhibited high correlations for all attributes, clearly demonstrated the relationships between blending ratio and flavor characteristics, and was accurate enough for practical use. FNN, thus, constitutes a powerful tool for accelerating product development. [source] Adaptive neuro-fuzzy models for the quasi-static analysis of microstrip lineMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 5 2008Celal Yildiz Abstract This article presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the effective permittivities and characteristic impedances of microstrip lines. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It has the advantages of expert knowledge of FISs and learning capability of artificial neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of ANFIS. The results of ANFIS are compared with the results of the experimental works, quasi-static methods, and a commercial electromagnetic simulator IE3D. There is very good agreement among the results of ANFIS models and quasi-static methods, IE3D, and experimental works. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1191,1196, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23322 [source] Fault Diagnosis Based on the Fuzzy-Recurrent Neural NetworkASIAN JOURNAL OF CONTROL, Issue 2 2001Zhao Xiang ABSTRACT A fuzzy-recurrent neural network (FRNN) has been constructed by adding some feedback connections to a feedforward fuzzy neural network (FNN). The FRNN expands the modeling ability of a FNN in order to deal with temporal problems. A basic concept of the FRNN is first to use process or expert knowledge, including appropriate fuzzy logic rules and membership functions, to construct an initial structure and to then use parameter-learning algorithms to fine-tune the membership functions and other parameters. Its recurrent property makes it suitable for dealing with temporal problems, such as on-line fault diagnosis. In addition, it also provides human-understandable meaning to the normal feedforward multilayer neural network, in which the internal units are always opaque to users. In a word, the trained FRNN has good interpreting ability and one-step-ahead predicting ability. To demonstrate the performance of the FRNN in diagnosis, a comparison is made with a conventional feedforward network. The efficiency of the FRNN is verified by the results. [source] Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profilingCANCER SCIENCE, Issue 10 2003Tatsuya Ando A fuzzy neural network (FNN) using gene expression profile data can select combinations of genes from thousands of genes, and is applicable to predict outcome for cancer patients after chemotherapy. However, wide clinical heterogeneity reduces the accuracy of prediction. To overcome this problem, we have proposed an FNN system based on majoritarian decision using multiple noninferior models. We used transcriptional profiling data, which were obtained from "Lymphochip" DNA microarrays (http://llmpp.nih.gov/DLBCL), reported by Rosenwald (N Engl J Med 2002; 346: 1937,47). When the data were analyzed by our FNN system, accuracy (73.4%) of outcome prediction using only 1 FNN model with 4 genes was higher than that (68.5%) of the Cox model using 17 genes. Higher accuracy (91%) was obtained when an FNN system with 9 noninferior models, consisting of 35 independent genes, was used. The genes selected by the system included genes that are informative in the prognosis of Diffuse large B-cell lymphoma (DLBCL), such as genes showing an expression pattern similar to that of CD10 and BCL-6 or similar to that of IRF-4 and BCL-4. We classified 220 DLBCL patients into 5 groups using the prediction results of 9 FNN models. These groups may correspond to DLBCL subtypes. In group A containing half of the 220 patients, patients with poor outcome were found to satisfy 2 rules, i.e., high expression of MAX dimerization with high expression of unknown A (LC_26146), or high expression of MAX dimerization with low expression of unknown B (LC_33144). The present paper is the first to describe the multiple noninferior FNN modeling system. This system is a powerful tool for predicting outcome and classifying patients, and is applicable to other heterogeneous diseases. [source] A direct control scheme based on recurrent fuzzy neural networks for the UPFC series branch,ASIAN JOURNAL OF CONTROL, Issue 6 2009Tsao-Tsung Ma Abstract In this paper, a new control scheme using recurrent fuzzy neural controllers is proposed for the series branch of the unified power flow controller (UPFC) to improve the dynamic performance of real-time power flow control functions with the aim of reducing the inevitable interaction between the real and reactive power flow control parameters. To simplify the theoretical analysis of the coupled dynamics within the UPFC and the controlled power system, the three phase description of a two-bus test power system embedded with a UPFC is transformed into d,q components based on a synchronously rotating reference frame. For control systems with inherent nonlinear coupling features, a feed-forward control scheme based on fuzzy neural controllers is developed to realize the decoupling control objectives. Based on the simulation results, the proposed control scheme is able to overcome the drawbacks of the traditional power flow controllers designed on small disturbance linearizing methods. Comprehensive simulation results on the EMTDC/PSCAD and MATLAB programs are presented and discussed to verify the effectiveness of the proposed control scheme. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] | |||||||||||||