Adaptive Neural Network-based Fuzzy Inference System Research Articles

The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS).

Banking risk measurement and management remain one of many challenges for managers and policymakers. This study contributes to the banking literature and practice in two ways by (a) proposing a risk ranking index based on the Mahalanobis Distance (MD) between a multidimensional point representing a bank's risk measures and the corresponding critical ratios set by the banking authorities and (b) determining the relative importance of a bank's risk ratios in affecting its financial standing using an Adaptive Neuro-Fuzzy Inference System. In this study, ten financial ratios representing five risk areas were considered, namely: Capital Adequacy, Credit, Liquidity, Earning Quality, and Operational risk. Data from 45 Gulf banks for the period 2016-2020 was used to develop the model. Our findings indicate that a bank is in a sound risk position at the 99%, 95%, and 90% confidence level if its Mahalanobis distance exceeds 4.82, 4.28, and 4.0, respectively. The maximum distance computed for the banks in this study was 9.31; only five out of the forty-five banks were below the 4.82 and one below the 4.28 and 4.0 thresholds at 3.96. Sensitivity analysis of the risks indicated that the Net Interest Margin is the most significant factor in explaining variations in a bank's risk position, followed by Capital Adequacy Ratio, Common Equity Tier1, and Tier1 Equity in order. The remaining financial ratios: Non-Performing Loans, Equity Leverage, Cost Income Ratio, Loans to Total Assets, and Loans to Deposits have the least influence in the order given; the Provisional Loans Ratio appears to have no influence.

A novel comparative model proposal for hospital pneumatic systems

Hospital pneumatic systems are of great importance in terms of efficient use of resources and early diagnosis of diseases. Each sample is transported at a constant and specific speed/pressure in existing pneumatic systems. This study aims to provide pressure/speed control according to the type of material being transported, prevent deterioration during transportation, and make a positive contribution to system users. We have proposed a new control model for hospital pneumatic systems. This hospital pneumatic system is modeled using fuzzy logic, adaptive neural network-based fuzzy inference system (ANFIS), and artificial neural network (ANN) methods. The parameters affecting the performance of the system are determined by experimental trials carried out. Fuzzy logic, ANFIS, and ANN models are compared using the regression coefficient (R2), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE) metrics to determine the most suitable modeling method for the system. As a result of this study, carrying the pressure value (velocity) ideal for the sample type will increase the energy efficiency of the system.

Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches

One of the most favorable renewable energy sources, solar photovoltaic (PV) can meet the electricity demand considerably. Sunlight is converted into electricity by the solar PV systems using cells containing semiconductor materials. A PV system is designed to meet the energy needs of King Abdulaziz University Hospital. A new method has been introduced to find optimal working capacity, and determine the self-consumption and sufficiency rates of the PV system. Response surface methodology (RSM) is used for determining the optimal working conditions of PV panels. Similarly, an adaptive neural network based fuzzy inference system (ANFIS) was employed to analyze the performance of solar PV panels. The outcomes of methods were compared to the actual outcomes available for testing the performance of models. Hence, for a 40 MW target PV system capacity, the RSM determined that approximately 33.96 MW electricity can be produced, when the radiation rate is 896.3 W/m2, the module surface temperature is 41.4 °C, the outdoor temperature is 36.2 °C, the wind direction and speed are 305.6 and 6.7 m/s, respectively. The ANFIS model (with nine rules) gave the highest performance with lowest residual for the same design parameters. Hence, it was determined that the hourly electrical energy requirement of the hospital can be met by the PV system during the year.

Data Modelling To Determine Room Rate with Adaptive Network Based Fuzzy Inference System And Particle Swarm Optimization

Determination of room rate in a hotel isinfluenced by two factors, namely internal andexternal. From an external perspective, PT. PIMhas eight competitor hotels that affect its roomrate. The Hotel Manager analyzes eachcompetitor's room rate changes to staycompetitive. Human analysis has severalshortcomings: subjectivity, fatigue andinconsistency. Then we need a decision supportor decision companion machine to determine theroom rate. ANFIS-PSO is a hybrid algorithmfrom the Adaptive neural network based fuzzyinference system (ANFIS) by utilizing ParticleSwarm Optimization (PSO) optimization.Traditional ANFIS is Gradient Decent (GD) asan algorithm for parameter optimization (model).This often happens to be stuck to get optimallocal results, to overcome this PSO is used as asolution. The results obtained from the ANFIS- PSO training contained a difference of Rp.3173,187 or a percentage of 1.34%. From themodeling obtained applied to the hotel PT.PIM,with the result of an increase in revenue of Rp.17,493,548. The conclusion obtained is thatANFIS-PSO can help managers to determine theroom rate by modeling data obtained from theANFIS-PSO method. Suggestion for thedevelopment of this research is that ANFIS-PSOhas a complex complexity of training algorithmsbecause there is a combination of twoalgorithms, so to make it better a differentalgorithm design is needed.

Application of neural network and weighted improved PSO for uncertainty modeling and optimal allocating of renewable energies along with battery energy storage

The power uncertainty of renewable energy encounters their optimal allocation with obstacle in an on-grid microgrids. Hence, power prediction is a logical tactic. Besides, optimal performance of battery energy storage for compensating electrical loads is another strategy. However, these two solutions must meet the economic expectations as well as reliability of customers. In this paper, generating section includes wind, solar and wave energies in which wind speed and solar irradiance are uncertain parameters and information of Hormoz Island, Iran is the input of problem. Hence, artificial neural network (DANN) is trained by three adaptive techniques to minimize the prediction error dynamically. Then, eco-statistic objective function, reliability criterion and efficient battery strategy are modeled thoroughly. After introducing five feasible scenarios, a heuristic algorithm which is weighted improved PSO, searches the global answers of optimization to test the efficiency of hybrid architecture. Moreover, forecasting results are compared with five adaptive neural network based fuzzy inference system (ANFIS) and one conventional Levenberg Marquardt (LM) based ANN for addressing the functionality of proposed DANN against the uncertainty of renewables. Consequently, simulation results of optimization are tested with heuristic algorithms to demonstrate the optimal answers of problem.

A neuro-fuzzy risk prediction methodology for falling from scaffold

Fall from height is one of the most significant safety issues in the construction industry, due to the high number of fatal injuries. Scaffolds are a leading cause and have one of the highest injury rates. Therefore, it is crucial to introduce preventive measures and strategies. This study introduces a hybrid approach that merges an Adaptive Neural Network-based Fuzzy Inference System (ANFIS) and a safety inspection checklist to identify risk factors and predict the risk of falling from scaffold on construction sites. Our findings indicate that platform, joints, ladders, personal protective equipment and guardrails are the most important factors. The approach can identify and assess key conditions and situations that have the greatest impact on fall risk. The hybrid ANFIS–checklist model is found to outperform the regression method in predicting fall risk. Experts can use also this approach in other safety areas to identify and predict workplace risk.

Hardware and software codesign methodology for implementing algorithms of person follower robotic system

Service sector is expecting more innovations in the field of robotics and automation due to manpower shortage, changes in society and increased level of affordability to the people. This paper presents development of an intelligent service robot to monitor activity of an elder person or differently abled person. In particular, this paper presents development of 'Person Follower Algorithms' and their implementation using hardware–software codesign methodology. This work also includes usage of adaptive neural network-based fuzzy inference system (ANFIS) for implementing a robotic task. The intelligent service robot is developed by integrating an IP camera with a NI single board rapid input output (sbRIO) which has steering ultrasonic sensor. The sbRIO is chosen to take the advantage of existing system and it is suitable for HW–software codesign. The efficacy of algorithms is validated by conducting a set of experiments in our research laboratory.

A Hybrid Method for Short-Term Wind Speed Forecasting

The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decomposed into both periodic and nonlinear series. Then, the ANFIS model is used to catch the nonlinear series and the SARIMA model is applied for the periodic series. Numerical testing results based on two wind sites in South Dakota show the efficiency of this hybrid method.

Artificial intelligent modeling to predict tensile strength of inertia friction-welded pipe joints

Adaptive neural network-based fuzzy inference system (ANFIS) is an artificial intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. The present paper proposes this novel technique of ANFIS to predict the tensile strength of inertia friction-welded tubular pipe joints with the aid of artificial neural network approach combined with the principle of fuzzy logic. The proposed model is multiple input–single output type of model which uses rotational speed and forge load as input signals. The set of rules has been generated directly from the experimental data using ANFIS. The performance of the proposed model is validated by comparing the predicted results with the actual practical results obtained by conducting the confirmation experiments. The application of χ 2 test confirms that the values of tensile strength predicted by proposed ANFIS model are well in agreement with the experimental values at 0.1 % level of significance. The proposed model can also be used as intelligent online adaptive control model for pipeline welding.

Artificial neural networks: a novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala.

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies.

Adaptive modeling of the drying of baker's yeast in a batch fluidized bed

This study investigates the drying of baker's yeast in a fluidized-bed dryer. Mathematical modeling of the process was performed, incorporating the important process and quality parameters of the system. Artificial neural network (ANN) and adaptive neural network-based fuzzy inference system (ANFIS) structures were used to create process and quality models. Due to uncertainty regarding the process parameters, various different ANN structures were built, and the ANN with the optimum performance results for the proposed models was selected. This study also presents an ANFIS modeling approach with adaptive structure. ANN quality modeling was performed using process output parameters, and the quality loss incurred from drying the product was determined. These proposed models are easy to apply and do not impose any additional burden on the process (or the employees). The database used in this work was gathered from large quantities of industrial data (about 570 batches) obtained under various working conditions at random times over one year.

A novel approach for ANFIS modelling based on full factorial design

Adaptive neural network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input–output data pairs of the system under consideration. The size of the input–output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper, we have proposed an ANFIS based system modelling where the number of data pairs employed for training is minimized by application of an engineering statistical technique called full factorial design. Our proposed method is experimentally validated by applying it to the benchmark Box and Jenkins gas furnace data and a data set collected from a thermal power plant of the North Eastern Electric Power Corporation (NEEPCO) Limited. By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced and thereby computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model.