scholarly journals The Bounded Capacity of Fuzzy Neural Networks (FNNs) Via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) With Its Applications

2014 ◽  
Vol 22 (6) ◽  
pp. 1373-1386 ◽  
Author(s):  
Jing Wang ◽  
Chi-Hsu Wang ◽  
C. L. Philip Chen
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Neural Networks ◽  
Inference System ◽  
Fuzzy Neural ◽  
Fully Connected ◽  
System F ◽  
Neural Fuzzy

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

scholarly journals Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

2011 ◽  
Vol 15 (1) ◽  
pp. 185-196 ◽  
Author(s):  
Y.-M. Chiang ◽  
L.-C. Chang ◽  
M.-J. Tsai ◽  
Y.-F. Wang ◽  
F.-J. Chang
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Fuzzy Neural Networks ◽  
Water Levels ◽  
Inference System ◽  
Rule Based ◽  
Pumping Stations ◽  
Fuzzy Neural ◽  
On Line ◽  
Model Efficiency

Abstract. Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

scholarly journals PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights

Axioms ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 14 ◽  
Author(s):  
Fernando Gaxiola ◽  
Patricia Melin ◽  
Fevrier Valdez ◽  
Juan Castro ◽  
Alain Manzo-Martínez
Keyword(s):  
Neural Networks ◽  
Fuzzy Numbers ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Fuzzy Neural Networks ◽  
Dynamic Adjustment ◽  
Inference System ◽  
Fuzzy Neural ◽  
Interval Type

A dynamic adjustment of parameters for the particle swarm optimization (PSO) utilizing an interval type-2 fuzzy inference system is proposed in this work. A fuzzy neural network with interval type-2 fuzzy number weights using S-norm and T-norm is optimized with the proposed method. A dynamic adjustment of the PSO allows the algorithm to behave better in the search for optimal results because the dynamic adjustment provides good synchrony between the exploration and exploitation of the algorithm. Results of experiments and a comparison between traditional neural networks and the fuzzy neural networks with interval type-2 fuzzy numbers weights using T-norms and S-norms are given to prove the performance of the proposed approach. For testing the performance of the proposed approach, some cases of time series prediction are applied, including the stock exchanges of Germany, Mexican, Dow-Jones, London, Nasdaq, Shanghai, and Taiwan.

scholarly journals Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

2010 ◽  
Vol 7 (5) ◽  
pp. 6725-6756 ◽  
Author(s):  
Y.-M. Chiang ◽  
L.-C. Chang ◽  
M.-J. Tsai ◽  
Y.-F. Wang ◽  
F.-J. Chang
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Fuzzy Neural Networks ◽  
Water Levels ◽  
Inference System ◽  
Rule Based ◽  
Pumping Stations ◽  
Fuzzy Neural ◽  
On Line ◽  
Model Efficiency

Abstract. Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

CHOQUET INTEGRAL–OWA BASED ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM WITH APPLICATION

2011 ◽  
Vol 10 (01) ◽  
pp. 15-34 ◽  
Author(s):  
YUANYUAN CHAI ◽  
LIMIN JIA
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Choquet Integral ◽  
Fuzzy Rule ◽  
Anfis Model ◽  
Inference System ◽  
Fuzzy Neural ◽  
Human Thinking ◽  
Neural Fuzzy

In order to solve the defects of consequent part expression in ANFIS model and several shortcomings in FIS, this paper presents a Choquet Integral–OWA based Fuzzy Inference System, known as AggFIS. This model has advantages in consequent part of fuzzy rule, universal expression of fuzzy inference operator and importance factor of each criteria and each rule, which is trying to establish fuzzy inference system that can fully reflect the essence of fuzzy logic and human thinking pattern. If we combine AggFIS with a feed forward-type neural network according to the basic principles of fuzzy neural network, we can obtain Choquet Integral–OWA based Adaptive Neural Fuzzy Inference System, which is named Agg-ANFIS. We apply this Agg-ANFIS model into the evaluation of traffic level of service. The experimental results show that Choquet Integral–OWA based Adaptive Neural Fuzzy Inference System (Agg-ANFIS) is a universal approximator because of its infinite approximating capability by training and can be used in complex systems modeling, analysis and prediction.

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