scholarly journals A Distributed Adaptive Neuro-Fuzzy Network for Chaotic Time Series Prediction

2015 ◽  
Vol 15 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Margarita Terziyska
Keyword(s):  
Time Series Prediction ◽  
Model Performance ◽  
Inference Mechanism ◽  
Neuro Fuzzy ◽  
Learning Procedure ◽  
Gradient Descent Algorithm ◽  
Chaotic Time Series Prediction ◽  
Takagi Sugeno ◽  
Fuzzy Network ◽  
Time Systems

Abstract In this paper a Distributed Adaptive Neuro-Fuzzy Architecture (DANFA) model with a second order Takagi-Sugeno inference mechanism is presented. The proposed approach is based on the simple idea to reduce the number of the fuzzy rules and the computational load, when modeling nonlinear systems. As a learning procedure for the designed structure a two-step gradient descent algorithm with a fixed learning rate is used. To demonstrate the potentials of the selected approach, simulation experiments with two benchmark chaotic time systems − Mackey-Glass and Rossler are studied. The results obtained show an accurate model performance with a minimal prediction error.

Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks

2008 ◽  
Vol 12 (4) ◽  
pp. 361-369 ◽  
Author(s):  
Felix Pasila ◽  
◽  
Ajoy K. Palit ◽  
Georg Thiele ◽  
◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Moving Average ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Training Algorithm ◽  
Electrical Load ◽  
Fuzzy Membership Functions ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
Fuzzy Network

The paper describes a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with additional moving average filter is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low SSE value with given training data of neuro-fuzzy network, are further fine tuned during the network training. Finally, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of electrical load time series.

A Takagi–Sugeno type neuro-fuzzy network for determining child anemia

2011 ◽  
Vol 38 (6) ◽  
pp. 7415-7418 ◽  
Author(s):  
Novruz Allahverdi ◽  
Ayfer Tunali ◽  
Hakan Işik ◽  
Humar Kahramanli
Keyword(s):  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
Fuzzy Network

ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS

2009 ◽  
Vol 08 (04) ◽  
pp. 429-444 ◽  
Author(s):  
CATHERINE VAIRAPPAN ◽  
SHANGCE GAO ◽  
ZHENG TANG ◽  
HIROKI TAMURA
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Chaotic Dynamics ◽  
Time Series Prediction ◽  
Data Series ◽  
Temporal Data ◽  
Optimal Solutions ◽  
Benchmark Data ◽  
Neuro Fuzzy ◽  
Fuzzy Network

A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately.

Modified neuro-fuzzy TSK network and its application in electronic nose

2013 ◽  
Vol 61 (3) ◽  
pp. 675-680
Author(s):  
S. Osowski ◽  
K. Brudzewski ◽  
L. Tran Hoai
Keyword(s):  
Electronic Nose ◽  
Theoretical Basis ◽  
Input Data ◽  
Clustering Algorithm ◽  
Inference Rules ◽  
Classification Problems ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
Fuzzy Network ◽  
Theoretical Considerations

Abstract The paper develops the modified structure of the Takagi-Sugeno-Kang neuro-fuzzy network with a theoretical basis for its adaptation. The simplified structure follows from the basic theoretical considerations concerning the way of creating the inference rules. The important point of this solution is the application of the fuzzy clustering algorithm to the input data. The efficiency of the proposed solution has been checked on the examples of regression and classification problems concerning the electronic nose.

Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks

2021 ◽  
pp. 1-18
Author(s):  
Glender Brás ◽  
Alisson Marques Silva ◽  
Elizabeth Fialho Wanner
Keyword(s):  
Genetic Programming ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Genetic Operators ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Gradient Based ◽  
Non Linear System ◽  
Takagi Sugeno ◽  
Fuzzy Network

This paper introduces a new approach to build the rule-base on Neo-Fuzzy-Neuron (NFN) Networks. The NFN is a Neuro-Fuzzy network composed by a set of n decoupled zero-order Takagi-Sugeno models, one for each input variable, each one containing m rules. Employing Multi-Gene Genetic Programming (MG-GP) to create and adjust Gaussian membership functions and a Gradient-based method to update the network parameters, the proposed model is dubbed NFN-MG-GP. In the proposed model, each individual of MG-GP represents a complete rule-base of NFN. The rule-base is adjusted by genetic operators (Crossover, Reproduction, Mutation), and the consequent parameters are updated by a predetermined number of Gradient method epochs, every generation. The algorithm uses Elitism to ensure that the best rule-base is not lost between generations. The performance of the NFN-MG-GP is evaluated using instances of time series forecasting and non-linear system identification problems. Computational experiments and comparisons against state-of-the-art alternative models show that the proposed algorithms are efficient and competitive. Furthermore, experimental results show that it is possible to obtain models with good accuracy applying Multi-Gene Genetic Programming to construct the rule-base on NFN Networks.

scholarly journals FAST MEDICAL DIAGNOSTICS USING AUTOASSOCIATIVE NEURO-FUZZY MEMORY

2017 ◽  
pp. 34-40
Author(s):  
Iryna Perova ◽  
Yevgeniy Bodyanskiy
Keyword(s):  
Fuzzy Inference ◽  
Medical Diagnostics ◽  
Evolving Systems ◽  
Neuro Fuzzy ◽  
Learning Procedure ◽  
Data Points ◽  
Pattern Retrieval ◽  
Inference Systems ◽  
Diagnostics System ◽  
Takagi Sugeno

This paper proposes an architecture of fast medical diagnostics system based on autoassociative neuro-fuzzy memory. The architecture of proposed system is close to traditional Takagi-Sugeno-Kang neuro-fuzzy system, but it is based on other principles. This system contains of recording subsystem and pattern retrieval subsystem, where diagnostics of patients with unknown diagnoses is realized. Level of memberships for all other possible diagnoses from recording subsystem is determined too. System tuning is based on lazy learning procedure and “neurons in data points” principle and uses bell-shaped fuzzy basis functions. Number of these functions changes during training process using principles of evolving connectionist systems. Bell-shaped membership functions centers can be tuned using proposed algorithm, processes of accumulation patients in fundamental memory and patients retrieval are described. This hybrid neuro-fuzzy associative memory combines advantages of fuzzy inference systems, artificial neural networks and evolving systems and its using provides the increasing of autoassociative memories capacity without essential complication of its architecture for medical diagnostics tasks.

scholarly journals COMPARATIVE STUDY OF MPPT BASED ON ARTIFICIAL INTELLIGENCE

2021 ◽  
Vol 9 (11) ◽  
pp. 625-639
Author(s):  
Rajonirina Solofanja Jeannie ◽  
◽  
Razafimahenina Jean Marie ◽  
Andrianaharison Yvon ◽  
Randriamasinoro Njakarison Menja ◽  
...  
Keyword(s):  
Simple Structure ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Neuro Fuzzy ◽  
Power Point ◽  
Intelligent Approach ◽  
Takagi Sugeno ◽  
Fuzzy Network

An MPPT or Maximum power point tracking command, associated with an intermediate adaptation stage, allows a photovoltaic generator (GPV) to operate in such a way as to continuously produce the maximum of its power. We present in this paper a new intelligent approach of a MPPT based on the hybrid and adaptive neuro-fuzzy network of ANFIS model. The latter is applied to a SEPIC* converter in order to extract at any time the maximum power available at the generator terminals and transfer it into the load, regardless of the sunshine variation as well as the temperature. The proposed method for a fixed and simple structure implements a Takagi-sugeno fuzzy system. Its performance will be confirmed by the comparison with the fuzzy logic command which is already known with its speed.

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