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

2014 ◽  
Vol 501-504 ◽  
pp. 391-394
Author(s):  
Yi Ming Xiang ◽  
Xue Yan Liu ◽  
Gui Xiang Ling ◽  
Bin Du
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Subtractive Clustering ◽  
Forecast Errors ◽  
Frost Heaving ◽  
Inference System ◽  
Neuro Fuzzy

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.

A Novel Neuro-Fuzzy Inference System with Multi-Level Membership Function for Classification Applications

2007 ◽  
Vol 11 (4) ◽  
pp. 365-372 ◽  
Author(s):  
Cheng-Jian Lin ◽  
◽  
Chi-Yung Lee ◽  
Cheng-Hung Chen ◽  
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Layer Structure ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Multi Level ◽  
Takagi Sugeno

In this paper, a novel neuro-fuzzy inference system with multi-level membership function (NFIS_MMF) for classification applications is proposed. The NFIS_MMF model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the NFIS_MMF model contains multi-level membership functions, which are multilevel activation functions. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), fuzzy entropy, and the backpropagation algorithm, is also proposed to construct the NFIS_MMF model and perform parameter learning. Simulations were conducted to show the performance and applicability of the proposed model.

scholarly journals AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FORECASTING AUSTRALIA’S DOMESTIC LOW COST CARRIER PASSENGER DEMAND

Aviation ◽  
2015 ◽  
Vol 19 (3) ◽  
pp. 150-163 ◽  
Author(s):  
Panarat Srisaeng ◽  
Glenn S. Baxter ◽  
Graham Wild
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Low Cost ◽  
Percentage Error ◽  
Subtractive Clustering ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Partition Method

This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

COMPARATIVE PERFORMANCE FOR PREDICTIVE MODELLING IN MOTOR INSURANCE CLAIMS

2016 ◽  
Vol 78 (12-2) ◽  
Author(s):  
Zuriahati Mohd Yunos ◽  
Siti Mariyam Shamsuddin ◽  
Razana Alwee ◽  
Noriszura Ismail ◽  
Roselina Salleh@Sallehuddin
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Predictive Modelling ◽  
Back Propagation Neural Network ◽  
Insurance Claims ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Motor Insurance ◽  
Claim Frequency

The expected claim frequency and the expected claim severity are used in predictive modelling motor insurance claims. There are two categories of claims were considered, namely, third party property damage and own damage. Datasets from the year 2001 to 2003 are used to develop the predictive model. This paper proposes three different methods, namely, regression analysis, back propagation neural network and adaptive neuro fuzzy inference system to model claim frequency and claim severity as the two important elements in modelling the motor insurance claims. The experimental results showed that the back propagation neural network model produces more accurate as compared to regression analysis and adaptive neuro fuzzy inference system in predicting the claim frequency and claim severity. For both OD and TPPD claim, the results have shown the lowest MAPE with 0.2191 and 0.6515, and 0.2169 and 0.326, respectively.

scholarly journals Fuzzy Versus Neuro-fuzzy Identification of Active Front Steering of a Vehicle on Slippery Roads

2019 ◽  
Vol 16 (1) ◽  
pp. 6078-6089
Author(s):  
B. Aalizadeh ◽  
A. Asnafi
Keyword(s):  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
High Rate ◽  
Identification Algorithm ◽  
Fuzzy Identification ◽  
Inference System ◽  
Identification Process ◽  
Active Front Steering ◽  
Neuro Fuzzy

Due to the high rate changes in the handling of cars, the use of an auxiliary identification process to design efficient controllers is of importance. Many identification algorithms have been proposed in the literature, which generally performs well under normal situations, but does not show acceptable performance in uncertain conditions. In this article, due to the nature of the neuro-fuzzy networks in identifying and predicting uncertain conditions, an adaptive neuro-fuzzy identification algorithm is proposed to steer vehicles at the uncertain slippery condition of roads. A set of data for three well-known manoeuvres of vehicle dynamics at conventional conditions was collected to train the algorithm using adaptive neuro-fuzzy inference system of MATLAB. Using back propagation of error as the learning algorithm, the parameters of the algorithm were modified regarding uncertain conditions. Making an analogy, the performance of the proposed identification scheme was compared to the untrained fuzzy one. In regular situations, the results were almost identical, but in uncertain ones such as slippery roads, the performance of the proposed neuro-fuzzy algorithm was much better.

scholarly journals ANALISA DAN PERBANDINGAN AKURASI MODEL PREDIKSI RENTET WAKTU ARUS LALU LINTAS JANGKA PENDEK

2015 ◽  
Vol 6 (3) ◽  
pp. 148
Author(s):  
Bambang Lareno
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy

<p>Abstrak <br />Terdapat banyak algoritma yang dapat dipakai untuk memprediksi arus lalu lintas, namun belum diketahui algoritma manakah yang memiliki kinerja lebih akurat untuk lalu lintas di Indonesia. Algoritma-algoritma tersebut perlu diuji untuk mengetahui algoritma manakah yang memiliki kinerja lebih akurat. Metode yang diusulkan adalah metode perbandingan tingkat akurasi dari algoritma berbasis neural network yang bisa digunakan untuk prediksi data rentet waktu. Algoritma yang akan diuji adalah back Propagation Neural Network (BP-NN), Adaptive Neuro Fuzzy Inference System (ANFIS), Wavelet Neural Network (WNN), dan Evolving Neural Network (ENN), yang digunakan untuk memprediksi arus lalulintas. Masing-masing algoritma akan implementasikan dengan menggunakan MatLab 2009b. Pengukuran kinerja dilakukan dengan menghitung rata-rata error yang terjadi melalui besaran Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) dan Mean Absolute Deviation (MAD). Semakin kecil nilai dari masing-masing parameter kinerja ini menyatakan semakin dekat nilai prediksi dengan nilai sebenarnya. Dalam penelitian ini diketahui bahwa Algoritma ENN memprediksi arus lalu lintas dengan lebih akurat.</p>

scholarly journals Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System

2010 ◽  
Vol 42 (1) ◽  
pp. 159-167
Author(s):  
Masumeh Sabet ◽  
Mehdi Naseri ◽  
Hosein Sabet
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Linear Process ◽  
Back Propagation Algorithm ◽  
Littoral Drift ◽  
Inference System ◽  
The Past ◽  
Neuro Fuzzy ◽  
Sand Drift

Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past, an accurate and reliable estimation of the rate of sand drift has still remained a problem. It is a non-linear process and can be described by chaotic time-series. The current study addresses this issue through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is about taking an initial fuzzy inference system (FIS) and tuning it with a back propagation algorithm based on the collection of input-output data. ANFIS was developed to predict the sand drift from a variety of causative variables. The structure and algorithm of ANFIS for predicting the rate of sand drift is described. The Adaptive Neuro-Fuzzy Inference System was validated by confirming its consistency with a database of specified physical process.

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

2016 ◽  
Vol 5 (4) ◽  
pp. 64-82 ◽  
Author(s):  
Shereen A. El-aal ◽  
Rabie A. Ramadan ◽  
Neveen I. Ghali
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Imagery Task ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

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