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.

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.

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.

SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS

2008 ◽  
Vol 16 (02) ◽  
pp. 245-274 ◽  
Author(s):  
BYOUNG-JUN PARK ◽  
WITOLD PEDRYCZ ◽  
SUNG-KWUN OH
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Inference Rule ◽  
Least Square Method ◽  
Fuzzy Neural Networks ◽  
Least Square ◽  
Rule Based ◽  
Fuzzy Partition ◽  
Fuzzy Neural ◽  
Input Variables

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models.

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.

Optimization of Operating Mode in Copper Flash Smelting Process

2014 ◽  
Vol 1046 ◽  
pp. 43-49
Author(s):  
Yi Yuan Shao ◽  
Fei Shao
Keyword(s):  
Neural Networks ◽  
Operating Mode ◽  
Fuzzy Neural Networks ◽  
Flash Smelting ◽  
Smelting Process ◽  
Mode Decomposition ◽  
Fuzzy Neural ◽  
On Line ◽  
Chaos Genetic Algorithm ◽  
Historical Samples

A batch of operating parameters which need to be resolved on line are represented by operating modes.Operating mode optimization for copper flash smelting process based on fuzzy neural networks is presented. First of all, the optimal samples set is screened from the historical samples set. Then mode decomposition based on fuzzy neural networks is used, and chaos genetic algorithm is used to rake the optimal operating sub-pattern.This way is used to copper flash smelting process.The simulation result shows that this way can guide production.

On-Line Prediction Model of Ultrasonic Polishing Surface Roughness

2010 ◽  
Vol 455 ◽  
pp. 539-543
Author(s):  
Ming Zhang ◽  
X.Q. Yang ◽  
Bo Zhao
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Fuzzy Neural Networks ◽  
Fuzzy Logic Systems ◽  
Identification Method ◽  
Fuzzy Neural ◽  
On Line

In order to solve the difficulty of on-line measuring the surface roughness of workpiece under ultrasonic polishing, the artificial neural networks and fuzzy logic systems are introduced into the on-line prediction model of surface roughness. The surface roughness identification method based on fuzzy-neural networks is put forward and used to the process of plane polishing. In the end, the on-line prediction model of surface roughness is established. The actual ultrasonic polishing experiments show that the accuracy of this prediction model is up to 96.58%, which further evidence the feasibility of the on-line prediction model.

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