Neuro-fuzzy Inference System Research Articles

Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids

With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.

A novel framework based on bi-objective optimization and LAN2FIS for Twitter sentiment analysis

Nowadays, opinion mining and sentiment analysis are the hot topics of research in social networks. Twitter is a popular social media that has general way of expressing opinions and interacting with other users in the online. Since the topics in Twitter are very diverse, it is difficult to collect data in the domain of sentiment classification. In this paper, a novel framework has been proposed which preprocesses information to enrich the tweet. Once the tweets are processed, a various features are extracted from the tweets. In this paper, we propose hybrid machine learning algorithms called LAN2FIS (logistic adaptive network based on neuro-fuzzy inference system) to utilize this huge information. The major challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes the system complex and difficult to train the learning algorithms in a feasible time and degrades the system model in terms of classification accuracy. Hence, feature selection becomes an essential task in proposing robust and efficient classification models while increasing the accuracy. This paper presents a bi-objective optimization (minimum redundancy and maximum relevancy) for feature selection, which finds the more efficient feature subsets can be obtained. We performed our experiments using public tweets. Our overall work is implemented in a parallel and distributed way using the Hadoop framework with the MongoDB database to solve the problem of the computation time of the analysis when the dataset of the tweets is very large. Finally, we evaluate the following the performance in terms of accuracy, precision, recall, F-measure and error rate. Experimental evaluations show that our H-MLA is more efficient and has higher accuracy than that of other classifiers.

Extracting water content from the ambient air in a double-slope half-cylindrical basin solar still using silica gel under Egyptian conditions

Direct generation of drinking water from ambient air using sunlight is of great interest for many populations. Here, a new desiccant mechanism using a highly hygroscopic silica gel was tested on the double-slope half-cylindrical basin solar still (DS-HCBSS) to extract the water content from the ambient air. As this mechanism is new to be applied into the solar still, an experimental and predictive study was conducted to investigate the thermal performance of the solar still under this modification. The modification was done in a double-slope half-cylindrical basin solar still, which has four longitudinal fins from inside to increase the surface area. The basin liner of the solar still and the fins were covered by a layer of silica gel with a thickness of 1.5 cm. A parabolic trough solar collector was used to raise the temperature inside the solar still during daytime solar illumination. The productivity of the DS-HCBSS with silica gel was enhanced by about 72% and 166% when longitudinal fins and longitudinal fins with gravels were used, respectively. Moreover, the efficiency of the DS-HCBSS with silica gel was increased by 15% and 35% when longitudinal fins and longitudinal fins with gravels were used, respectively. The best accumulated productivity (400 mL/m2) was recorded for the DS-HCBSS with silica gel, longitudinal fins, and gravels. Finally, a Neuro-fuzzy Inference System (ANFIS) model was employed to predict the process response (productivity) of the investigated systems under different process input factors (ambient temperature and solar irradiance). The predictive model obtained a good agreement with the experimental results. The experimental results revealed that the double-slope half-cylindrical basin solar still with the modification provides a new concept for Air Water Harvesting (AWH) and atmospheric water generation.

Modeling of the Safety Climate and the Cultural Attitudes to Predict Unsafe Behaviors Using the Neuro-Fuzzy Inference System (ANFIS)

Background: Unsafe behavior in industries can be due to different factors. The aim of this study was to predict and model unsafe behavior using a safety atmosphere and cultural attitudes questionnaires. Methods: This study was a descriptive-analytic and cross-sectional examination that analyzed the data and predicted the unsafe behaviors of 90 construction workers using Neuro-Fuzzy Inference System (ANFIS) in MATLAB R2016a software. Results: In this study, the model of the safety atmosphere - unsafe behavior and the model of the cultural attitudes - unsafe behavior had the regression coefficients of 0.93373 and 0.9234, respectively. It showed that each of the parameters has a close relationship to the rate of the unsafe behavior. In this regard, a combination of the safety atmosphere and safety attitude parameters for the estimation of the unsafe behaviors achieved the better results with a regression coefficient of 0.9453 which indicates the direct effect of both parameters simultaneously on unsafe behavior. Conclusion: Based on the findings, it can be concluded that the neuro-fuzzy model can be used as an appropriate tool for predicting unsafe behavior in the industries.

Reduced-order modeling of dynamic stall using neuro-fuzzy inference system and orthogonal functions

To consider stall flutter in the design procedure of a blade, accurate models of flow loading are needed. This paper first presents a numerical simulation of an airfoil undergoing a deep dynamic stall employing a computational fluid dynamics code. Overset and polyhedral grid techniques are adopted to accurately simulate the flow field at high angles of attack. Having validated the simulation, the occurrence of stall flutter over a pitching airfoil with an increase in amplitude and frequency of oscillations is examined. The results express that the amplitude of the lift and pitching moment depends on the amplitude of the forced oscillation and there are higher harmonics of the pitching moment compared to the forced oscillation frequency content, both indicating the nonlinearity of aerodynamic lift and pitching moment. Subsequently, a nonlinear reduced model of the dynamic stall is derived using a fuzzy inference system (FIS) and the adaptive network-based FIS (ANFIS). Due to the unsatisfactory results of modeling, especially at post-stall angles of attack, the Gram–Schmidt orthogonalization technique is used to construct a more complex structure of the input variables. The new higher-order input variables have been re-employed by FIS and ANFIS. The results show that excellent modeling is achieved by ANFIS between the new structure of the inputs and the corresponding aerodynamic coefficients using only 10% of input–output data. Having found an appropriate relation, the proposed reduced-order model could properly predict the aerodynamic response of the pitching airfoil at two reduced frequencies.

Estimation of apparent shear stress of asymmetric compound channels using neuro-fuzzy inference system

Discharge calculations and conveyance estimations in rivers are of prime importance during high flow events. Conventional methods using single global resistance parameters are incapable of assessing discharge with high precision; thus incorporating apparent shear into these methods not only improves the results but also addresses the momentum transfer phenomena over sub-sections. Apparent shear stress modelling for asymmetric channels with different geometric and roughness parameters is found to be monotonic and tedious in linear regression. To improve its accuracy and efficiency, one of the modelling tools is to apply adaptive network-based fuzzy inference system (ANFIS). In this paper, apparent shear stress is trained, modeled and tested for a wide range of asymmetric channels including various experimental and field data. It is found that asymmetrical channels have large positive shear force acting at the vertical imaginary interface between the floodplain and main channel. The positive apparent shear force indicates that the slower floodplain flow retards the faster flow in the main channel. Therefore, considering correct shear stress in the discharge estimation will result in higher accuracy. The application of ANFIS in modelling the output variable of apparent shear stress is done using five geometrical and hydraulic parameters of varying range from small-scale experiments to real field measurements as the input variables. The coefficient of determination (R2) for the modeled apparent shear is 0.96, which is exceptionally high as comparison to past models. Furthermore, comparison of discharge calculation using modeled apparent shear stress and other conventional methods has shown that the results obtained using vertical interface shear stress have higher accuracy.

A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System

In the rolling stock sector, the ability to protect passengers, freight and services relies on heavy inborn maintenance. Initiating an accurate model suitable to foresee the change of attitude on components when operating rolling stock systems will assist in reducing lock down and favors heavy productivity. In that light, this paper showcases a suitable methodology to track degradation of components through the blinding of physic laws and artificial intelligent techniques. This model used to foresee failure deterioration rate and remaining useful life (RUL) speculation is case study to showcase its quality and perfection, within which behavioral data are obtained through simulated models initiated in Mathlab. For feature extraction and forecasting issues, different neuro-fuzzy inference systems are designed, learnt and authenticated with powerful outputs gained during this process.

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

A Novel Fuzzy Inference Approach: Neuro-fuzzy Cognitive Map

In this study, a new approach based on fuzzy cognitive map (FCM) and neuro-fuzzy inference system (NFIS), called the neuro-fuzzy cognitive map (NFCM), is proposed. Here, the NFCM is used for diagnosis of autoimmune hepatitis (AIH). AIH is a chronic inflammatory liver disease. AIH primarily affects women and typically responds to immunosuppressive therapy with clinical, biochemical, and histological remission. An untreated AIH can lead to scarring of the liver and ultimately to liver failure. If rapidly diagnosed, AIH can often be controlled by medication. NFCM is a new extension of FCM, which employs a NFIS to determine the causal relationships between concepts. In the proposed approach, weights are calculated using the knowledge and experience of experts as well as the advantages of NFIS. This makes the presented model more accurate. Having a high convergence speed, the proposed NFCM model performs well by achieving an AIH diagnosis accuracy of 89.81%. The superiority of the proposed NFCM model over the conventional FCM is that, it uses the NFIS to determine the link weights which train system parameters.

Utilization of Artificial Intelligence for Sensitivity Analysis in the Stock Market

The main contribution of this paper is to perform sensitivity analysis using artificial intelligence methods on the US stock market using alternative psychological indicators. The Takagi-Sugeno fuzzy model applies investor sentiment represented by VIX index and monitors the impact of economic optimism, political stability and control of the corruption index on the S&P 500 stock index. Alternative psychological indicators have been chosen that have not been explored in the context of stock index performance sensitivity. Investors primarily use fundamental and technical analysis as a source to determine when and what to buy into an investment portfolio. However, psychological factors that may indicate the strength of reaction to the market are often neglected. Fuzzy rules are determined and tested using a neuro-fuzzy inference system and then the rules are reduced by fuzzy clustering to improve performance of ANFIS. The membership function is defined as a Gaussian function because it has the least RMSE value. The sensitivity analysis confirmed that there is a significant impact of the political stability index and the economic optimism index on the S&P 500 performance. Conversely, the sensitivity analysis, unlike the previous study, did not confirm the strong impact of VIX on equity index performance. Results indicate that incorporating psychological indicators in macroeconomic models leads to better supervision and control of the financial markets.

Power oscillations damping using wide-area-based solar plant considering adaptive time-delay compensation

The aim of this paper is to investigate the use of the ever increasing penetration of renewable energy into the power grid to solve challenging problems such as inter area power oscillations without the use of expensive power electronic devices and power system stabilizers. The increase in size of interconnected power systems, energy demand, and installation of remote renewable energies with relatively weak tie-lines has witnessed different stability problems such as low-frequency inter-area oscillations. Inter-area oscillation reduces system stability and transmission capacity. Without effective damping control mechanism, these oscillations could prolong and threaten the security of the system. This paper proposes a supplementary controller from a photovoltaic (PV) solar plant for damping inter-area oscillations. Due to its strong correlation to active power flow and monitoring system stress, area phase-angle difference is employed for the remote signal input of the controller. The signal can be obtained from phasor measurement unit (PMU) through wide-area measurements systems (WAMS). To deal with a wide range of variable delay in the signal input, an adaptive compensator is designed to reduce the impact of the communication latency using neuro-fuzzy inference system. A two-area four-machine test system is used and simulated with a Simulink-based package developed for the work of this study. The time-domain simulations, modal and frequency response analysis demonstrate the capability of the proposed controller to effectively damp inter-area oscillations, under a small- and large-scale disturbances and against a wide range of time delays.

Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network

Efficient prediction of Open Pit mining blast induced ground vibration, has an important role in reduction of the environmental complaints. This paper proposed a new hybrid evolutionary artificial neural network (ANN) optimized by genetic algorithm (GA) to predict peak particle velocity (PPV). The proposed GA-ANN suggests a systematic and automated way to find out a proper ANN architecture namely; number of neurons, activation functions, training algorithm and number of epochs. A data set consisting of maximum charge weight per delay, horizontal distance (HD), radial distance (RD) and a new modified radial distance (MRD) between monitoring and blasting station provided at Sungun Copper Mine site in Iran were used to validate the proposed approach. Comparing the performance of the proposed GA-ANN model by statistical indices indicate the superiority of the GA-ANN model against the empirical predictors and neuro-fuzzy inference system. As an important finding, incorporating MRD instead of the conventional distance measures of HD and RD improves the accuracy of the prediction. Finally results signify the efficiency of the proposed GA-ANN approach in finding optimum architecture of ANN while trying to predict PPV.

Brief Paper: Evaluation of Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means based ANFIS System in Diagnosis of Alzheimer

Brief Paper: Evaluation of Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means based ANFIS System in Diagnosis of Alzheimer

A new Neuro-Fuzzy Inference System with Dynamic Neurons (NFIS-DN) for system identification and time series forecasting

A new Neuro-Fuzzy Inference System with Dynamic Neurons or NFIS-DN is presented here for discrete time dynamic system identification and time series forecasting problems. The proposed dynamic system based neuron, referred to as Dynamic Neuron (DN) is realized by a discrete-time nonlinear state-space model. The DN is designed such way, that the output considers only the effect of finite past instances, enabling the system with finite memory. The NFIS-DN model has five layers, and DNs are employed only in the layers handling crisp values. The antecedent and the consequent parameters of NFIS-DN are updated using a self-regulated backpropagation through time learning algorithm. The performance evaluation of NFIS-DN has been carried-out using benchmark problems in the areas of nonlinear system identification and time series forecasting. The results are compared with the state-of-the-art method on the neural fuzzy networks. The obtained results clearly suggest that the NFIS-DN performs significantly better while using a smaller or similar number of fuzzy rules. Finally the practical application of the NFIS-DN has been demonstrated using two real-world problems.

Qualitative Metrics on Breast Cancer Diagnosis with Neuro Fuzzy Inference Systems

Qualitative Metrics on Breast Cancer Diagnosis with Neuro Fuzzy Inference Systems

Hybrid data fusion model for restricted information using Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system

Information fusion is the crux of data fusion which is used to compare large numerical data using normalization and aggregate function. The recent trends in information fusion have the limitations in fusing the important details in order to overcome the security issues in both low-level and high-level information fusion systems. The information from heterogeneous sources is different from one another like conceptual, contextual and graphical. This information is included for the fusion process, but the ancient approach has the limitation, and this research work proposes a new model of information fusion for the restricted content for various types of information. In this research work, an algorithm for information fusion is implemented for decision making based on Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system. Proposed hybrid work is applicable in data fusion process based on the theoretical approach of Dempster–Shafer and then into ANFIS. The proposed data fusion method is employed in a wide range of fields which include robotics, statistics, estimation and control.

Performance improvement in vertical heterogeneous handoff methodology using CANFIS classification approaches

SummaryDetection of malicious Base Stations (BS) in heterogeneous wireless networks is significant for developing an efficient system in wireless networks. In this paper, the handoff is performed between two different networks such as Wi‐Fi and WiMax, which may be located in different locations in the same region. The trust features, Cumulative Binary Features and Cost Index Features of each BS in different wireless network environment, are derived individually, which differentiates the behavior of the normal BS and malicious BS in different networks using Co‐Active Neuro Fuzzy Inference System (CANFIS) classification approach. The proposed methodology is compared with other state‐of‐arts methods for evaluating the individuality and efficiency. The proposed heterogeneous handoff methodology achieves 96.02% of packet delivery ratio, 90% detection rate, 94.6% of precision, and 15.99 ms of latency.

Approximation of the Dependence of the Radius of the Atomic Nucleus on Its Parameters Using a Fuzzy Hybrid Network Model

In the present investigation, the model of the hybrid network in the form of an adaptive system neuro-fuzzy inference is developed for approximating the dependence of the radius of an atomic nucleus vs. its charge and mass number. With the use of developed model, the radii of 84 nuclides for which experimental data on their sizes were not available were estimated.

Prediction of thiophene removal from diesel using [BMIM][AlCl4] in EDS Process: GA-ANFIS and PSO-ANFIS modeling

ABSTRACTIn this study, the treatment capability of 1-butyl-3-methylimidazolium tetrachloroaluminate [BMIM] [AlCl4] ionic liquid was investigated in a diesel fuel containing thiophene. The effects of operating parameters including the volumetric ratio of ionic liquid to model fuel, extraction temperature and time have been studied. Then, a neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed to predict the sulphur removal from diesel fuel using [BMIM][AlCl4] in EDS process. The outputs were in a good agreement with the experimental data, and the model tuned by PSO predicts the data better than ANFIS and ANFIS tuned by GA.

Composite braking AMT shift strategy for extended-range heavy commercial electric vehicle based on LHMM/ANFIS braking intention identification

In order to improve the safety and braking energy recovery rate of the composite braking system for extended-range heavy commercial electric vehicle, AMT shift control strategy is studied based on Layering Hidden Markov Model/Adaptive Neuro-fuzzy Inference System (LHMM/ANFIS) braking intention identification model. Firstly, according to the requirement of the AMT shift strategy in braking process, the braking intentions were classified into normal braking condition and emergency braking condition. Then combined with the composite braking force distribution, the motor braking power generation characteristics and the critical condition of dangerous working state, the AMT shift strategy was analyzed and established under two braking conditions. Finally, to verify the control effect, the verification test was carried out with the initial braking speed of 60 km h−1 under the normal braking condition and the emergency braking condition separately on the hardware in the loop simulation platform based on A&D 5435 and the testing vehicle. Meanwhile, simulation study was completed in Matlab/Simulink under NEDC_90 cycle condition. The experiment and simulation results show that the developed AMT shift control strategy can accurately identify the braking intention, and the transmission shifts correctly according to corresponding conditions, which can also make the motor operating points closer to the high efficiency area. Therefore, the AMT shift control strategy proposed in this paper can effectively improve the braking energy recovery rate, and ensure the braking safety and stability.