scholarly journals An intelligent Bayesian hybrid approach to help autism diagnosis

2021 ◽  
Author(s):  
Paulo Vitor de Campos Souza ◽  
Augusto Junio Guimaraes ◽  
Vanessa Souza Araujo ◽  
Edwin Lughofer
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Autistic Traits ◽  
Fuzzy Rules ◽  
Human Beings ◽  
Real World Data ◽  
Inference System ◽  
Fuzzy Neural ◽  
Adolescents And Adults

AbstractThis paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.

Optimized neural network and adaptive neuro-fuzzy controlled dynamic voltage restorer for power quality performance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Prashant Kumar ◽  
Sabha Raj Arya ◽  
Khyati D. Mistry
Keyword(s):  
Neural Network ◽  
Power Distribution ◽  
Fuzzy Inference ◽  
Dynamic Performance ◽  
Hybrid Approach ◽  
Voltage Regulation ◽  
Dynamic Voltage Restorer ◽  
Inference System ◽  
Intelligence System ◽  
Neuro Fuzzy

Abstract In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform.

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

2021 ◽  
pp. 181-189
Author(s):  
Wayan Firdaus Mahmudy ◽  
Aji Prasetya Wibawa ◽  
Nadia Roosmalita Sari ◽  
H. Haviluddin ◽  
P. Purnawansyah
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Online Journal ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Network Weight

Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.

PERFORMANCE EVALUATION OF FUZZY SINGLE LAYER WEIGHTLESS NEURAL NETWORK

2007 ◽  
Vol 15 (03) ◽  
pp. 381-393 ◽  
Author(s):  
RAIDA AL-ALAWI
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fuzzy Inference ◽  
Single Layer ◽  
Learning Phase ◽  
Fuzzy Rules ◽  
Test Bed ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Pattern

The paper evaluates the performance of a neuro-fuzzy pattern classification system based on the weightless neural network architecture. The system utilizes a Single Layer Weightless Neural Network (SLWNN) to extract the features vector that measures the similarity of the input pattern to the different classification groups. In contrast to the traditional crisp Winner-Takes-All (WTA) classification scheme used by SLWNN, our system uses a Fuzzy Inference System (FIS) for classification. The network is trained by a hybrid learning scheme that combines a single pass learning phase for training the SLWNN followed by a supervised learning phase for extracting a set of fuzzy rules suitable to classify the training set. The FIS learns fuzzy rules from the feature vectors generated by the SLWNN for the set of training patterns. The recognition of handwritten numerals is employed as a test-bed to demonstrate the effectiveness of the proposed neuro-fuzzy system. Experimental results show that the performance of the proposed system surpasses the performance of the traditional SLWNN.

A FUZZY NEURAL NETWORK MODEL FOR FUZZY INFERENCE AND RULE TUNING

1994 ◽  
Vol 02 (03) ◽  
pp. 265-277 ◽  
Author(s):  
KEON-MYUNG LEE ◽  
DONG-HOON KWANG ◽  
HYUNG LEEK WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Target System ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Fine Tune

It is relatively easy to create rough fuzzy rules for a target system. However, it is time-consuming and difficult to fine-tune them for improving their behavior. Meanwhile, in the process of fuzzy inference the defuzzification operation takes most of the inferencing time. In this paper, we propose a fuzzy neural network model which makes it possible to tune fuzzy rules by employing neural networks and reduces the burden of defuzzification operation. In addition, to show the applicability of the proposed model we perform an experiment and present its result.

scholarly journals Deduction of reservoir operating rules for application in global hydrological models

2018 ◽  
Vol 22 (1) ◽  
pp. 831-851 ◽  
Author(s):  
Hubertus M. Coerver ◽  
Martine M. Rutten ◽  
Nick C. van de Giesen
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Water Cycle ◽  
Anthropogenic Impacts ◽  
Fuzzy Rules ◽  
Uncertain Information ◽  
Hydrological Models ◽  
Inference System ◽  
Reservoir Release ◽  
And Storage

Abstract. A big challenge in constructing global hydrological models is the inclusion of anthropogenic impacts on the water cycle, such as caused by dams. Dam operators make decisions based on experience and often uncertain information. In this study information generally available to dam operators, like inflow into the reservoir and storage levels, was used to derive fuzzy rules describing the way a reservoir is operated. Using an artificial neural network capable of mimicking fuzzy logic, called the ANFIS adaptive-network-based fuzzy inference system, fuzzy rules linking inflow and storage with reservoir release were determined for 11 reservoirs in central Asia, the US and Vietnam. By varying the input variables of the neural network, different configurations of fuzzy rules were created and tested. It was found that the release from relatively large reservoirs was significantly dependent on information concerning recent storage levels, while release from smaller reservoirs was more dependent on reservoir inflows. Subsequently, the derived rules were used to simulate reservoir release with an average Nash–Sutcliffe coefficient of 0.81.

The Study of System Model Based on Fuzzy Inference and Neural Network

2012 ◽  
Vol 197 ◽  
pp. 547-552
Author(s):  
Ming Ming Gao ◽  
Liang Shan
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Least Square ◽  
Fuzzy Rules ◽  
System Model ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Anfis Method

For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. This paper gives the simulation example of modeling a typical system with ANFIS method and good result is obtained.

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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