Adaptive Network Fuzzy Inference System Research Articles

Design and Optimization of Distribution Routes Using Evolutionary Strategy and Type-1 Singleton Neuro-Fuzzy Systems

This paper presents a new proposal to design and to optimize the routes by hybrid intelligent model using evolutionary strategies and Adaptive Network Fuzzy Inference System (ANFIS). The hybridization uses an evolutionary computing model to create random a priori routes in the first step. The model serves as the inputs to the singleton type-1 ANFIS system that provides the knowledge and changes the connections between customers or sub-routes for distances by approximation. The generated routes solved by ANFIS model in the second phase will be optimized in the last phase of the proposal with the evolutionary strategy. The goal of this proposal is to provide a simple way to optimize routes without a hard mathematical model that produces an optimal or sub-optimal solution without an optimization function reducing the complexities of classical approaches.

Artificial Intelligence for Inferential Control of Crude Oil Stripping Process

Stripper columns are used for sweetening crude oil, and they must hold product hydrogen sulfide content as near the set points as possible in the faces of upsets. Since product quality cannot be measured easily and economically online, the control of product quality is often achieved by maintaining a suitable tray temperature near its set point. Tray temperature control method, however, is not a proper option for a multi-component stripping column because the tray temperature does not correspond exactly to the product composition. To overcome this problem, secondary measurements can be used to infer the product quality and adjust the values of the manipulated variables. In this paper, we have used a novel inferential control approach base on adaptive network fuzzy inference system (ANFIS) for stripping process. ANFIS with different learning algorithms is used for modeling the process and building a composition estimator to estimate the composition of the bottom product. The developed estimator is tested, and the results show that the predictions made by ANFIS structure are in good agreement with the results of simulation by ASPEN HYSYS process simulation package. In addition, inferential control by the implementation of ANFIS-based online composition estimator in a cascade control scheme is superior to traditional tray temperature control method based on less integral time absolute error and low duty consumption in reboiler.

Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms

Advanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution. Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.

Design of Adaptive Fractional-Order PID Controller to Enhance Robustness by Means of Adaptive Network Fuzzy Inference System

In this paper, a tuning strategy for the design of fractional-order proportional–integral–derivative (PI λ D µ ) controllers is proposed. First, a PI λ D µ controller is designed with genetic algorithm in order to obtain the training data. Then, three Adaptive Network Fuzzy Inference System (ANFIS) structures, related to K p , K i and K d parameters of the PI λ D µ controller, are formed by using the training data. These ANFIS structures are used in the PI λ D µ controller instead of K p , K i and K d parameters, and they are capable of self-tuning during the simulation based on the input signal of the adaptive PI λ D µ controller (ANFIS–PI λ D µ ). Finally, in order to show the control performance and robustness of the proposed parameters adjustment method with ANFIS, simulation results are obtained by using the MATLAB–Simulink program for two different systems and the results obtained from ANFIS–PI λ D µ controller are compared with the results of PI λ D µ and fuzzy logic controller.

Specific K-mean clustering-based perceptron for dengue prediction

Traditional neural networks come up with drawback relating to choosing the number of nodes in each layer. This paper proposes a novel adaptive network fuzzy inference system (ANFIS) to overcome the aforementioned problem. In particular, we use incremental k-mean to pre-identify the number of nodes in the adaptive network. Each node includes a set of samples in a training set. For each sample, we identify a fuzzy value of the particular sample data belonging to each node in the network. The learning perceptron algorithm also investigates to adjust weights by learning from real output data. In this study, the novel ANFIS model is employed to the dengue prediction application as well as evaluates performance execution by a real dataset of dengue disease in Tien Giang, Vietnam. The result shows that our proposed model of ANFIS gets better accuracy in comparison with linear regression, multiple linear regression, time series and neural network.

Predicting Real-Time Crash Risk for Urban Expressways in China

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

A new hybrid method for time series forecasting: AR–ANFIS

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR---ANFIS). AR---ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR---ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR---ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

An Effective Approach of CT Lung Segmentation Using Possibilistic Fuzzy C-Means Algorithm and Classification of Lung Cancer Cells with the Aid of Soft Computing Techniques

Lung cancer is one of the most common lethal type of diseases. One of the most important and difficult tasks a doctor has to carry out is the detection and diagnosis of cancerous lung cells from the Computed Tomography (CT) images result. Segmentation and classification of lung CT image, based on soft computing, is still a challenging task in the medical field, due to more computational time and accuracy. This paper deals with an improvement in lung cancer detection using Possibilistic Fuzzy C-Means (PFCM) based segmentation. This work also focuses on the normal and abnormal cancer cells that is classified by using the algorithms of SVM (Support Vector Machine), Gaussian Interval Type II Fuzzy Logic System and Genetic Algorithm (SVMFLGA). The results demonstrate that the SVMFLGA outperforms the Gaussian Interval type II fuzzy logic system (GAIT2FLS) in terms of classification accuracy. Keywords: Adaptive Network Fuzzy Inference System (ANFIS), Fuzzy Possibilistic C-Means Algorithm (FPCM), Gray Level Co-Occurrence Matrix (GLCM), Non-Small Cell Lung Carcinoma (NSCLC), Small Cell Lung Carcinoma (SCLC), Type II Fuzzy Logic System (T2FLS).

Real-Time Prediction of Temperature for Electromagnetic Heating Therapy in Deep-Seated Tissue

This paper aims to develop a mathematical model for predicting the temperature response of tissues in electromagnetic heating therapy (EHT), when using a magnetic flux concentrator to improve heating efficiency. EHT has two critical challenges when applied to deep-seated tissue heating, i.e., the temperature might not be accurately measured and the magnetic field intensity decreases with increasing depth. The finite-element method (FEM) is suitable for coupled analysis with electromagnetic fields and heat transfer, which can be used to predict temperature profiles in deep tissue implanted with magnetic materials. To improve the accuracy of the FEM model, an adaptive network fuzzy inference system (ANFIS) model is implemented on the basis of measured data and simulated data, which were generated by the FEM model. The ANFIS model can provide a large number of testing data to optimize the parameters in the FEM model; moreover, it can be used to expedite the optimization process.

Modelling Intelligent Water Resources Allocation for Multi-users

This study proposes intelligent water resources allocation strategies for multiple users through hybrid artificial intelligence techniques implemented for reservoir operation optimization and water shortage rate estimation. A two-fold scheme is developed for (1) knowledge acquisition through searching input–output patterns of optimal reservoir operation by optimization methods and (2) the inference system through mapping the current input pattern to estimate the water shortage rate by artificial neural networks (ANNs). The Shihmen Reservoir in northern Taiwan is the study case. We first design nine possible water demand conditions by investigating the changes in historical water supply. With the nine designed conditions and 44-year historical 10-day reservoir inflow data collected during the growth season (3 months) of the first paddy crop, we first conduct the optimization search of reservoir operation by using the non-dominated sorting genetic algorithm-II (NSGA-II) in consideration of agricultural and public water demands simultaneously. The simulation method is used as a comparative model to the NSGA-II. Results demonstrate that the NSGA-II can suitably search the optimal water allocation series and obtain much lower seasonal water shortage rates than those of the simulation method. Then seasonal water shortage rates in response to future water demands for both sectors are estimated by using the adaptive network fuzzy inference system (ANFIS). The back-propagation neural network (BPNN) is adopted as a comparative model to the ANFIS. During model construction, future water demands, predicted monthly inflows (or seasonal inflow) of the reservoir in the next coming quarter and historical initial reservoir storages configure the input patterns while the optimal seasonal water shortage rates obtained from the NSGA-II serve as output targets (training targets) for both neural networks. Results indicate that the ANFIS and the BPNN models produce almost equally good performance in estimating water shortage rates, yet the ANFIS model produces even better stability. The reliability of the proposed scheme is further examined by scenario analysis. The scenario analysis indicates that an increase in public water demand or a decrease in agricultural water demand would bring more impacts of water supply on agricultural sectors than public sectors. Similarly, a bigger decrease in inflow amount would obviously bring more influence on agricultural sectors than public one. Consequently, given predicted inflow, decision makers can pre-experience the possible outcomes in response to competing water demands through the estimation models in order to determine adequate water supply as well as preparedness measures, if needed, for drought mitigation.

Predicting Geotechnical Investigation Using the Knowledge Based System

The purpose of this paper is to evaluate the optimal number of investigation points and each field test and laboratory test for a proper description of a building site. These optimal numbers are defined based on their minimum and maximum number and with the equivalent investigation ratio. The total increments of minimum and maximum number of investigation points for different building site conditions were determined. To facilitate the decision-making process for a number of investigation points, an Adaptive Network Fuzzy Inference System (ANFIS) was proposed. The obtained fuzzy inference system considers the influence of several entry parameters and computes the equivalent investigation ratio. The developed model (ANFIS-SI) can be applied to characterize any building site. The ANFIS-SI model takes into account project factors which are evaluated with a rating from 1 to 10. The model ANFIS-SI, with integrated recommendations can be used as a systematic decision support tool for engineers to evaluate the number of investigation points, field tests, and laboratory tests for a proper description of a building site. The determination of the optimal number of investigative points and the optimal number of each field test and laboratory test is presented on reference case.

Enhancing Engineering Education via Physical Experiments: The Case of Learning Energy Storage with a Flywheel System

Abstract Design thinking is complex that is hard to learn and harder to teach. The purpose of engineering education is to graduate engineers who can design (Dym CL, 2005). How to enhance students’ reasoning and cognizing about data has gained an increasingly visible role in the K-12 curriculum (Doerr H. M. and Lyn D., 2003). In order to enhance teaching and learning engineering mathematics, a modeling approach by shifting the focus on finding solutions to a particular problem to create a system is proposed in this study. Besides, a training process for the college students who are intending to be the instructors of guiding physical experiments is included. Nonlinear systems, signifying any system is not linear, cannot satisfy the superposition principle. In this paper, a new modeling method for nonlinear systems based on an Adaptive Network-Fuzzy Inference System (ANFIS) for operation and practice was used (Jang, J.S.R., 1993). Through this method, students were able to learn how the transfer functions work for identifying an unknown system. This method had well helped the participated students how the nature of nonlinear system is and how to find out the optimal solutions in controls.

Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation

The most important meteorological variables influencing plant growth are temperature, moisture, and solar radiation. Because it is scarcely gauged at meteorological stations in Turkey, solar radiation is commonly estimated by artificial neural networks (ANN), adaptive network-fuzzy inference system (ANFIS), multiple linear regression (MLR) models, or by empirical equations relating it to available meteorological data at monthly periods composed differently for each one of 12months. In general, the explanatory meteorological data comprise month number, extraterrestrial radiation, average air temperature, average relative humidity, average sunshine duration, and daylight hours. Such data together with solar radiation measured by the Turkish State Meteorological Service (MGM) at 163 stations having records of at least 20years are used in monthly units in developing the models. First, as a result of a variance inflation factors analysis, calendar month number (M), extraterrestrial radiation (Ra), average air temperature (Tmean), and average relative humidity (RHmean) are determined to be meaningful explanatory variables for estimation of solar radiation. Second, various combinations of input variables are dissected using the ANFIS and ANN models. Next, the accuracies of the ANFIS, ANN, and MLR models, and of Angstrom, Abdalla, Bahel, and Hargreaves–Samani empirical equations are compared with each other. The final results show that the ANN model performs better than the ANFIS and MLR models and the empirical equations in estimating solar radiation in Turkey.

Variable bit rate video traffic prediction based on kernel least mean square method

In this study, the problem of variable bit rate (VBR) video traffic prediction is addressed. VBR traffic prediction is necessary in dynamic bandwidth allocation for multimedia quality of service control strategies. Autoregressive (AR) models have been widely used in VBR traffic prediction where the least mean square (LMS)-based methods were utilised for parameter estimation. However, they are ineffective when the traffic is dynamic in nature. In this study, using the Brock, Dechert, and Scheinkman (BDS) test, it is shown that the video traffic is non-linear. Kernel is an efficient tool to convert non-linear data into linear one in a higher-dimensional space. The kernel LMS (KLMS) method is proposed to forecast the next frame sizes of I, B and P frames as well as the next group-of-pictures (GOP) size of video traffic. Extensive simulations were performed on different video traces where different performance metrics were considered. KLMS results were very close to those of the Wiener–Hopf optimum solution and better than the results of commonly used normalised LMS and other algorithms such as the least mean kurtosis (LMK), wavelet LMK, adaptive network fuzzy inference system (ANFIS) and neural networks.

Design of ANFIS Estimator of Permanent Magnet Brushless DC Motor Position for PV Pumping System

This paper presents a new scheme for PMBLDC (permanent magnet brushless direct current) rotor position estimation based an ANFIS (adaptive network fuzzy inference system) estimator. The operation of such motor requires accurate rotor position knowledge. However, most of rotor position sensors produce undesirable effects such as mechanical losses and have other disadvantages. In order to overcome the disadvantages, sensorless scheme seems to offer great advantages. This work present an ANFIS estimator design. Combining the adaptive capability of the neural network to gather with the reasoning ability of the fuzzy logic in ANFIS modeling results in a fast responding and flexible model. This procedure lends itself perfectly adapted for complex system such as PV pumping systems.

A Comparative Study of Type 1 Singleton Fuzzy Logic Systems in Machining Application

This paper presents a comparison of two different techniques of Soft Computing (SC), in union with a novel approach of a hybrid model with Central Composite Design (CCD) with capacity of performing a rule reduction, without loss of reliability, and also performs a reduction of the computational time spent in prediction. Results obtained with this method show better performance thought generating a minimum error rate when it is compared with the Adaptive Network Fuzzy Inference Systems (ANFIS), or with the least squares method in case of pure the Fuzzy Singleton system.

Adaptive Network Based Fuzzy Inference System For Speech Recognition Through Subtractive Clustering

Fuzzy modeling require two main steps which are structure identification and parameter optimization, the first one determines the numbers of membership functions and fuzzy if-then rules, while the second identifies a feasible set of parameters under the given structure. However, the increase of input dimension, rule numbers will have an exponential growth and there will cause problem of “rule disaster”. In this paper, we have applied adaptive network fuzzy inference system ANFIS for phonemes recognition. The appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system. First learning of the network structure by subtractive clustering, in order to define an optimal structure and obtain small number of rules, then learning of parameters network by hybrid learning which combine the gradient decent and least square estimation LSE to find a feasible set of antecedents and consequents parameters. The results obtained show the effectiveness of the method in terms of recognition rate and number of fuzzy rules generated.

A New Algorithm to Model Highly Nonlinear System based Coactive Neuro Fuzzy Inference System

investigated. Membership Function Generation and its mapping to Neural Network are introduced. .System modeling based on conventional mathematical tools (differential equations) is not well suited for dealing with ill-defined and uncertain systems. By contrast, a fuzzy inference system employing fuzzy if- then rules can model the qualitative aspects of human knowledge and reasoning process without employing precise quantitative analyses. An adaptive network fuzzy inference system (ANFIS) is introduced, and Multiple Inputs /Outputs Systems (Extended ANFIS Algorithm) is implemented. A Modification algorithm of ANFIS, Coupling of ANFIS called coactive neuro fuzzy system (CANFIS), is introduced and implemented. The software of the modified algorithm of MIMO model identification is built and generated by me or added as a toolbox to matlab. To test the validity of the modified algorithm ANFIS (CANFIS algorithm), a coupled inputs-outputs example is simulated from the numerical equation. The result of modified algorithm (CANFIS) showed a conformance with the simulated example and the root mean square (RMSE) is very small.

An Adaptive Network Fuzzy Inference System Approach for Site investigation

Abstract Site investigation has to be effective and must be carried out in a systematic way. The purpose of this article is to evaluate the number of investigation points, field tests, and laboratory tests for a description of a building site. Such an assessment depends on many parameters based on experiences which cannot be physically evaluated. The guidance on spacing is available from many sources, and such guidance provides a starting point for the extent of investigation. The recommendations were examined and used for building of the model to predict the optimal number of investigation points. Several parameters with the biggest influence on the number of investigation points were considered. The influence of each parameter was determined on the basis of recommendations and engineering judgment. Increments of the minimum number of investigation points for a different building site conditions were used to construct the model with adaptive network fuzzy inference system (ANFIS). The formed ANFIS-SI model was applied on reference cases. There is a good agreement between the model and the reference cases. Additionally, the recommendations for the type and frequency of tests in each stratum are provided to optimize the soil investigation. The ANFIS-SI model, with integrated recommendations, can be used as a systematic decision support tool for engineers to evaluate the number of investigation points for a description of the building site.

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties. Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.</p>