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Intelligent estimation of flood hydrographs using an adaptive neuro-fuzzy inference system (ANFIS)

MODELING EARTH SYSTEMS AND ENVIRONMENT 2017年第1期3卷 1页

作者： Pahlayani, Hamid Dehghani, Amir Ahmad Bahremand, Abdol Reza Shojaei, Saeed Gorgan Univ Agr Sci & Nat Resources Gorgan Iran Gorgan Univ Agr Sci & Nat Resources Watershed Management Engn Gorgan Iran Gorgan Univ Agr Sci & Nat Resources Water Engn Gorgan Iran Yazd Univ Coll Nat Resources & Desert Dept Management Arid & Desert Reg Yazd Iran

Intelligent computing tools such as artificial neural networks (ANNs) and fuzzy logic approaches are proven to be efficient when these are individually applied to variety of problems. Recently, there has been a growing interest in combining both of those approaches, and as a result, neuro-fuzzy computing technique has been evolved. This approach has been tested and evaluated in the field of signal processing and related areas. But researches have only begun evaluating the potential of this hybrid approach to hydrologic modeling studies. This paper presents the application of an adaptive neuro-fuzzy inference system (ANFIS) to flood hydrograph modeling, and it is demonstrated by modeling the flood hydrograph flowing to the Shirindarreh Reservoir dam located in the northern Khorasan Province, Iran. This was carried out using 24 flood hydrographs recorded in Barbarqaleh river gauging station. From this dataset, 15 flood hydrographs were chosen to train the model and 6 flood hydrographs to test the model. The different architectures of neuro-fuzzy model according to the membership function and learning algorithm were designed and trained with different epochs. The results were evaluated in comparison with the observed hydrographs and the best structure of model was chosen according the least RMSE in each performance. To evaluate the efficiency of neuro-fuzzy model, various statistical indices such as Nash-Sutcliff and flood peak discharge error criteria were calculated to show the level of agreement between observed and calculated hydrographs. In this simulation, the coordinates of a flood hydrograph including peak discharge were estimated using the discharge values occurred in the earlier time steps as input values to the neuro-fuzzy model. The amount of Nash-Sutcliff criterion for the input patterns of P1, P2, P3 and P4 were 0.82, 0.81, 0.62 and 0.45, respectively. These results indicate the satisfactory efficiency of neuro-fuzzy model, especially in P1, P2 and P3 for

关键词： Artificial neural network Flood hydrograph simulation Fuzzy logic hybrid learning algorithm Neuro-fuzzy model Shirindarreh Reservoir dam

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Water Quantity Prediction Based on Particle Swarm Optimization and Evolutionary algorithm Using Recurrent Neural Networks

Water Quantity Prediction Based on Particle Swarm Optimizati...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Nian Lai, Shuhua Univ Dist Columbia Dept Elect & Comp Engn Washington DC 20008 USA

ISBN: (纸本)9781424496365

Stormwater pollution is one of most important issues that the District of Columbia faces. Urban stormwater pollution can be a large contributor to the water quality problems of many receiving waters, as runoff transports a wide spectrum of pollutants to local receiving waters and their cumulative magnitude is large. Therefore, evaluations of stormwater runoff quantity are necessary to enhance the performance of an assessment operation and develop better water resources management and plan. However, some computational intelligence methods that have most successful applications on time series prediction have not yet been investigated on water quantity prediction. Only a limited number of neural networks models were applied to the water quantity monitoring. Therefore, we proposed an Elman style based recurrent neural network on the water quantity prediction. A hybrid learning algorithm incorporating particle swarm optimization and evolutional algorithm was presented, which takes the complementary advantages of the two global optimization algorithms. The neural networks model was trained by particle swarm optimization and evolutional algorithm to forecast the stormwater runoff discharge. The USGS real-time water data at Four Mile Run station at Alexandria, VA were used as time series input. The excellent experimental results demonstrated that the proposed method provides a suitable prediction tool for the stormwater runoff monitoring.

关键词： Elman style based recurrent neural network Neural networks Particle swarm optimization Prediction algorithms Rivers Time series analysis Water pollution Water resources computational intelligence method cumulative magnitude environmental science computing evolutionary algorithm evolutionary computation global optimization algorithm hybrid learning algorithm hydrology learning (artificial intelligence) particle swarm optimisation particle swarm optimization recurrent neural nets stormwater pollution stormwater runoff monitoring stormwater runoff quantity time series time series prediction water pollution water quality water quality water quantity monitoring water quantity prediction water resources water resources management

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An ANFIS Based Model for Predicting Frost Heaving in Seasonal Frozen Regions

An ANFIS Based Model for Predicting Frost Heaving in Seasona...

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3rd International Conference on Civil Engineering and Transportation (ICCET 2013)

作者： Xiang Yiming Liu Xueyan Ling Guixiang Du Bin Guizhou Commun Planning Survey & Design Inst Co L Guiyang 550022 Peoples R China

ISBN: (纸本)9783038350057

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computational speed, forecast errors, and efficiency. The ANFIS based model proves to be an effective approach to achieve both high accuracy and less computational complexity for predicting frost heaving.

关键词： Frost heaving hybrid learning algorithm subtractive clustering algorithm adaptive network-based fuzzy inference system

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ANFIS Based Intelligent Control of Vector Controlled Induction Motor Drive

ANFIS Based Intelligent Control of Vector Controlled Inducti...

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2nd International Conference on Emerging Trends in Engineering and Technology (ICETET09)

作者： Gupta, R. A. Kumar, Rajesh Surjuse, Rajesh S. MNIT Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9781424452507

This paper presents Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent control of vector controlled induction motor drive. The proposed intelligent control scheme consists of sensorless adaptive neuro-fuzzy speed controller with speed estimation based on adaptive neuro-fuzzy inverse model. The proposed neuro-fuzzy speed controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The conventional PI speed controller is replaced by Adaptive Neuro-Fuzzy Inference System (ANFIS), which tunes the fuzzy inference system with hybrid learning algorithm. This makes fuzzy system to learn. The structure of speed estimator is also same as speed controller but it incorporates inverse model identification algorithm for speed estimation. The performance of the proposed scheme is investigated at different operating conditions. The simulation study indicates robustness and suitability of drive for high performance drives applications.

关键词： Adaptive Neuro-Fuzzy Inference System (ANFIS) back propagation algorithm hybrid learning algorithm inverse model speed estimator

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Design of a Robust and Adaptive Fuzzy Logic based Power System Stabilizer (RAFLPSS) for damping low frequency electro-mechanical oscillations

Design of a Robust and Adaptive Fuzzy Logic based Power Syst...

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4th International Conference on Machine Vision (ICMV) - Machine Vision, Image Processing and Pattern Analysis

作者： Mahabuba, A. Khan, M. Abdullah Ahmed, Edriss Ali Al Ghurair Univ Coll Engn & Appl Sci Dubai U Arab Emirates BS Abdurrahman Univ Sci & Technol Dept EEE Madras Tamil Nadu India

ISBN: (纸本)9780819490254

This paper presents a design procedure for a Robust and Adaptive Fuzzy Logic based Power System Stabilizer (RAFLPSS) to improve the small signal stability of Power System. The parameters of RAFLPSS are tuned by adaptive neural network. This RAFLPSS uses ANFIS network (Adaptive Network based Fuzzy Inference System) which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFLPSS. The dynamic performance of SMIB system with the proposed RAFLPSS under different operating conditions and change in system parameters has been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic based PSS (FPSS) are compared with the proposed RAFLPSS. The simulation results demonstrate that the proposed RAFLPSS performs well in damping and quicker response when compared with the other two PSSs.

关键词： Small Signal Stability RAFLPSS ANFIS Network hybrid learning algorithm Single Machine Infinite Bus (SMIB) System low frequency oscillations

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Damping local and inter-area oscillations with Adaptive Neuro-Fuzzy power system stabilizer

Damping local and inter-area oscillations with Adaptive Neur...

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41st International Universities Power Engineering Conference

作者： Mitra, P. Chowdhury, S. Chowdhury, S. P. Pal, S. K. Song, Y. H. Taylor, G. A. Jadavpur Univ Kolkata India Brunel Univ Uxbridge UB8 3PH Middx England

ISBN: (纸本)9781861353429

In this paper, the authors report on the design, simulation and validation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) based Power System Stabilizer (PSS) for a single-machine-infinite-bus (SMIB) and a multi-machine power system and investigate its performance in damping low frequency local and inter-area oscillations. The design employs a first order Sugeno fuzzy model, whose parameters are tuned off-line through hybrid learning algorithm. This algorithm is a combination of Least Square Estimator and Err or Backpropagation method. The performance of the ANFIS-based PSS is observed through digital simulation for both SMIB and multi-machine systems. Finally the results are compared with conventional fuzzy PSS performances. It is observed that ANFIS-based PSS is playing more satisfactory role in damping local and inter-area oscillations, which proves its effectiveness in small-signal stability.

关键词： PSS Adaptive Neuro-Fuzzy Inference System Sugeno-fuzzy model hybrid learning algorithm

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Diagnosis of Pulmonary Tuberculosis using Fuzzy Inference System 2

Diagnosis of Pulmonary Tuberculosis using Fuzzy Inference Sy...

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2nd IEEE International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH)

作者： Ekata Tyagi, Praveen Kumar Gupta, Neeraj Kumar Gupta, Shivam KIET Dept Appl Sci Ghaziabad 201206 India KIET Dept Elect & Elect Engn Ghaziabad 201206 India KIET Dept Comp Sci Engn Ghaziabad 201206 India

ISBN: (纸本)9781467390804

Artificial intelligence is a set of real time computational methodologies to address complex real-world problems. In this paper, Neurofuzzy Inference System for analysis of pulmonary tuberculosis (TB) disease is discussed. For effective result, simulation is being done by using the realistic causes of pulmonary TB. The Neurofuzzy system is used for decision making based on a predefined rule based upon the symptoms of the patient are taken as inputs and the corresponding TB risk quotient is evaluated as the output. This crisp result obtained allows us to diagnose the low or high risk of the disease in the patient. hybrid learning algorithm is applied for minimization of error in the output.

关键词： Artificial Intelligence Neuro Fuzzy system hybrid learning algorithm Pulmonary Tuberculosis Disease

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Design and Comparative analysis of Genetic algorithm tuned Fractional and Integer order PI controllers with Adaptive neurofuzzy controller for speed control of indirect vector controlled Induction motor

Design and Comparative analysis of Genetic algorithm tuned F...

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作者： Girma Kassa ADDIS ABABA UNIVERSITY

学位级别：硕士

This thesis presents design and comparative analysis of fractional order PI controller, integer order PI controller and an adaptive neurofuzzy controller trained by input output data from fractional order PI controller for indirect vector controlled induction motor. The parameters of the two PI controllers were genetically optimized using square of error as a fitness function. The proposed neurofuzzy controller trained with input and output data of fractional order PI controller incorporates fuzzy logic algorithm with a multilayer artificial neural network structure using hybrid learning algorithm. This improves the performance of induction motor drive. The fractional order model of induction motor has been also investigated using simulation results and it was inferred that optimized model of induction motor is an integer order model. The performance of adaptive neurofuzzy inference system controller, was compared with fractional and integer order PI controllers using MATLAB simulation results with different operating conditions. It was observed from the simulation results that by using ANFIS, FOPI, and IOPI controllers, for the reference speed of 50 rad/sec, the percentage peak overshoots were 0.496%,13.068% and 15.698% respectively. Thus, ANFIS shows dramatic decrease in overshoot. Also the speed reaches its desired set value at 0.15 second in ANFIS controlled IM drive. These show the effectiveness of the designed neurofuzzy controller and the designed neurofuzzy controller tries to speed up the performance of IM drive. On the other hand, FOPI controller showed better performance than IOPI controller for IM drive, this is because of FOPI controller has one additional parameter for tuning which is integration order.

关键词： Fractional order controller Induction Motor Indirect vector control Adaptive Neurofuzzy Inference System Artificial Neural Network Fuzzy logic hybrid learning algorithm

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An intelligent strategy map to evaluate improvement projects of auto industry using fuzzy cognitive map and fuzzy slack-based efficiency model

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 151卷 106920-106920页

作者： Rezaee, Mustafa Jahangoshai Yousefi, Samuel Baghery, Majid Chakrabortty, Ripon K. Urmia Univ Technol Fac Ind Engn Orumiyeh Iran Allameh Tabatabai Univ Fac Management & Accounting Tehran Iran UNSW Canberra ADFA Sch Engn & Informat Technol Canberra ACT Australia

Effective selection of improvement projects is a basic step to enhance an organization's superiority. If improvement projects are not defined and selected properly, the improvement plan is prone to risks. Conventionally, these projects are created using self-assessment processes based on an intelligent strategy map, which is done in terms of functional perspectives of Balanced Scorecard (BSC). However, due to its static behavior, BSC needs to be reviewed under dynamic conditions and based on strategic goals. In this study, to remove this shortcoming, cause, and effect relationships between goals and performance measurements in organizations' BSC are investigated. This is accomplished by using the Fuzzy Cognitive Map (FCM) method. Having modification capability over time, this FCM method is suitable for grasping changing strategies of organizations and their competitors. Furthermore, based on the BSC, the effectiveness value of each proposed improvement project on the organization's goals is calculated considering FCM and a hybrid learning algorithm. Then, improvement projects based on the organization's goals and resource constraints are prioritized using fuzzy Slack-Based Data Envelopment Analysis (SBDEA). Integrating these methods provides a unique decision support system tool for decision makers. Finally, to evaluate the efficiency and accuracy of the proposed approach, a real case study on an automotive-parts supplier is presented along with its results.

关键词： Intelligent strategy map Balanced scorecard Fuzzy cognitive map Fuzzy slack-based data envelopment analysis hybrid learning algorithm Automotive industry

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A power-efficient and re-configurable analog artificial neural network classifier

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MICROELECTRONICS JOURNAL 2021年 111卷 105022-105022页

作者： Mohamed, Ahmed Reda Qi, Liang Wang, Guoxing Shanghai Jiao Tong Univ Dept Micronano Elect Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China

This paper presents a power-efficient and re-configurable radial basis function-artificial neural network (RBFANN) for real-time pattern classification tasks. We have developed a MATLAB-based behavioral model, which can be employed to facilitate the off-chip learning of the proposed network with a hybrid learning algorithm. Besides, a generic design methodology is presented to ease implementing different scales of neural networks. For the RBF-ANN, an analog tunable Gaussian kernel circuit is used as an activation neuron while a current-mode computation is employed to improve the power efficiency. The proposed network is designed and simulated in 0.18 ??????m X-FAB CMOS process for a non-linear XOR-pattern classification and voice activity detector (VAD), respectively. It is found that the network?s weights obtained using the developed model in MATLAB are well matched with the Spectre simulation (Virtuoso) results with a maximum relative error of 7.1%. Consequently, the proposed model can be effectively employed for the off-chip training of RBF-ANN. Besides, the preliminary simulation results of the VAD depict that the classification accuracy reaches as large as 95% in a noisy environment of babble with an acoustic signal to noise ratio (SNR) of 10 dB. Meanwhile, the power efficiency of the proposed network gets significantly improved compared to the recent prior arts.

关键词： Analog radial basis function Artificial neural network Classifier Gaussian kernel Current-mode processing hybrid learning algorithm

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