Adaptive Neuro-fuzzy Approach Research Articles

Enhancing pitch angle control in variable speed doubly fed induction generator based wind energy conversion system using advanced adaptive neuro-fuzzy approach with particle swarm optimization

Pitch angle control is a common method used to smooth out output power fluctuations in the wind turbines. This paper focuses on Particle Swarm Optimization (PSO) based Adaptive Neuro-Fuzzy Inference System (ANFIS) pitch angle control scheme for a variable speed Doubly Fed Induction Generator (DFIG) based wind energy conversion system designed for operation in high wind speed regions. The proposed enhanced adaptive controller comprises both Sugeno type fuzzy inference system (FIS) and neural network architecture. In the Sugeno type FIS, the membership functions are optimized using a revisited PSO method. The primary objective is to control the pitch angle to ensure that generator power and speed operate within desired references, reducing fluctuations and minimizing mechanical blade stress. A MATLAB/SIMULINK model of wind energy conversion system setup is prepared and simulations are conducted using different control methods. The effectiveness of the proposed approach is confirmed based on the simulation results.

Transformer hot spot temperature estimation through adaptive neuro fuzzy inference system approach

Transformer performance and efficiency can be enhanced by effectively address the properties of its insulation system. The power transformer insulation system weakens as a result of operational thermal stresses brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial metric that must be maintained below the prescribed limit while power transformers are operating so as to maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging effect of insulation depends on transitions in hot spot temperatures. Due to the non‐linear nature of the conventional mathematical models used to determine these temperatures, and complexity of thermal phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique. The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oil-filled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132 kV power transformers under dynamic operating regimes. The ANFIS model results were validated against field measured values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation. The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and 0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct determination of HST can help asset managers in thermal analysis trending of the in-service transformers, helping them to make proper loading recommendations for safeguarding the asset.

A hybrid adaptive neuro-fuzzy approach for automatic spoken digit recognition

A hybrid adaptive neuro-fuzzy approach for automatic spoken digit recognition

A robust identification method for inherent parameters of lithium battery considering thermal distribution and state of charge

In charge/discharge processes, the internal resistances of lithium-ion batteries (LIBs) change dynamically with the current, temperature and state of charge (SOC). However, these resistances are usually assumed to be constant after their value is offline identified, and the influence of the factors, especially SOC and temperature distribution on them is ignored. Besides, noise is less considered during the identification, resulting in poor robustness of the model. Aiming for these problems, a robust identification approach is proposed for the inherent parameters of soft pack LIBs in consideration with the effect of temperature distribution and state of charge as data is polluted by noise. First, to solve the modeling problem of the thermal process with unknown dynamics and unknown boundaries, a spatiotemporal least square support vector machine (LS-SVM) model is developed to reconstruct the temperature distribution of the LIB. Then, a robust adaptive neuro fuzzy (R-ANFIS) approach is developed to construct the model between the internal resistances and the external parameters, i.e., SOC, current and distributed temperature. It develops the kernel strategy to reduce the model dimension and minimizes both mean and variance of error to improve the model robustness. Using experiments at different discharge rates, the effectiveness of this method is tested and verified.

Application of novel hybrid machine learning approach for estimation of ultimate bond strength between ultra-high performance concrete and reinforced bar

There are few studies on how ultra-high-performance concrete UHPCs interact with reinforced bar, and there is less data on non-proprietary UHPCs. On the other hand, experimental testing (pull-out tests), which determine the feature of the reinforced bars in UHPC, requires more time, more cost, and high error percentage in the results. Therefore, the purpose of this research is to assess the variables that affect the ultimate bond strength (UBS) between UHPC and deformed reinforcing bars in order to generate guidelines for the novel connection of field-cast UHPC. To forecast the UBS between UHPC and reinforced bars, enhanced machine learning (ML) models, such as support vector regression (SVR), artificial neural networks (ANN), and an adaptive neuro-fuzzy approach (ANFIS) have been developed, using the root mean squared error (RMSE), correlation coefficient (r), and coefficient of determination (R2). Referring and synthesizing the feature parameter selection, concrete compressive strength(fc), tensile strength, bond length, water/cement ratio, reinforcing bar strength, and bar diameter (L/D) are used as input to machine learning models derived from pull out test. The R2 values for ANN, SVR, and ANFIS were 0.8825, 0.9351, and 0.9848, respectively. The RMSE value for ANN, SVR and ANFIS were 0.8779, 0.8555, 0.7667, respectively, all representing the best performance ANFIS in this analysis, followed by SVR and ANN as the weakest analysis. The numerical relevance of several components from three models demonstrates that the proportion of embedded depth to reinforcing bar diameter has a considerable influence on UHPC bond strength that is consistent with experimental results.

An adaptive neuro-fuzzy approach to predict the thermal efficiency of differently configured solar flat plate water collector systems

An adaptive neuro-fuzzy approach to predict the thermal efficiency of differently configured solar flat plate water collector systems

Adaptive neuro-fuzzy approach for maximum power point tracking with high gain converter for photo voltaic applications

Adaptive neuro-fuzzy approach for maximum power point tracking with high gain converter for photo voltaic applications

Experimental Investigation and Prediction of Mechanical Properties in a Fused Deposition Modeling Process

Additive manufacturing, also known as three-dimensional printing, is a computer-controlled advanced manufacturing process that produces three-dimensional items by depositing materials directly from a computer-aided design model, usually in layers. Due to its capacity to manufacture complicated objects utilizing a wide range of materials with outstanding mechanical qualities, fused deposition modeling is one of the most commonly used additive manufacturing technologies. For printing high-quality components with appropriate mechanical qualities, such as tensile strength and flexural strength, the selection of adequate processing parameters is critical. Experimentally, the influence of process parameters such as the raster angle, printing orientation, air gap, raster width, and layer height on the tensile strength of fused deposition modeling printed items was examined in this work. Through analysis of variance, the impact of each parameter was measured and rated. The system’s response was predicted using an adaptive neuro-fuzzy technique and an artificial neural network. In Minitab software, the Box-Behnken response surface experimental design was used to generate 46 experimental trials, which were then printed using acrylonitrile butadiene styrene polymer materials on a three-dimensional forge dreamer II fused deposition modelling printing machine. The results revealed that the raster angle, air gap, and raster width had significant impacts on the tensile strength. The adaptive neuro-fuzzy approach and artificial neural network predicted tensile strength accurately with an average percentage error of 0.0163 percent and 1.6437 percent, respectively. According to the findings, the model and experimental data are in good agreement.

Adaptive Neuro-Fuzzy Approach for Solar Radiation Forecasting in Cyclone Ravaged Indian Cities: A Review

The measurement of solar radiation and its forecasting at any particular location is a difficult task as it depends on various input parameters. So, intelligent modeling approaches with advanced techniques are always necessary for this challenging activity. Adaptive neuro-fuzzy inference system (ANFIS) based on modeling plays a vital role in the selection of relevant input parameters for undertaking precise solar radiation prediction. Numerous literature works focusing on ANFIS-based techniques have been reviewed during the estimation of solar energy incidents in the eastern part of India. During solar forecasting, the input parameters considered for this model are the duration of the sunshine, temperature, and humidity whereas the clearness index value has been considered as an output parameter for calculation. For designing the model, practical data sets have been prepared for some specified locations. Finally, the outcome is compared with several other techniques. During this course of analysis, several studies have been reviewed for a comprehensive literature survey work.

Optimisation of software release time using adaptive neuro fuzzy approach

Optimisation of software release time using adaptive neuro fuzzy approach

Optimisation of software release time using adaptive neuro fuzzy approach

Cost and reliability are two important factors that govern the release time of software. These parameters are estimated with respect to mean number of failures in the software. A software release time growth model is used to determine the reliability of the software. The most important decision taken by the software development team is to release a software with maximum reliability and minimum cost. Researchers have discussed various optimisation methods to find the optimal release time. In this research study, an adaptive neuro fuzzy optimisation method is discussed with different input criteria to find the optimal time of release of software. Different input criteria considered in this research are cost and reliability of the software. Adaptive neuro fuzzy optimisation method has the advantage of fuzzy logic as well as neural network. Numerical examples are explained and results are compared with the other optimisation methods. From the results of the numerical example, it can be concluded that the neuro fuzzy optimisation method is best for software release time optimisation.

Usability estimation of component-based software system using adaptive neuro fuzzy approach

Cost effective development is the prime goal for software developers. To achieve this goal, nowadays, component-based software system (CBSS) are developed. In CBSS, the existing components are reused to develop a new software system which increases the reusability of components. It also reduces time and efforts of software developers, which is cost effective. The success or failure of software system depends on its usability. Usability can increase the market revenue. So, to increase the acceptance rate of CBSS among the users, it is important to evaluate the usability of CBSS before the software is released. In this paper, usability of CBSS is evaluated based on four input factors by using two widely used soft computing techniques, i.e., fuzzy logic and adaptive neuro fuzzy logic. Experimental results obtained from both the techniques are also compared and it is observed that ANFIS approach reduces the error rate and provide more accurate results. This research work will help the software developers to estimate the usability of CBSS in a more efficient manner.

Biomedical image retrieval using adaptive neuro-fuzzy optimized classifier system.

The quantity of scientific images associated with patient care has increased markedly in recent years due to the rapid development of hospitals and research facilities. Every hospital generates more medical photographs, resulting in more than 10 GB of data per day being produced by a single image appliance. Software is used extensively to scan and locate diagnostic photographs to identify patient's precise information, which can be valuable for medical science research and advancement. An image recovery system is used to meet this need. This paper suggests an optimized classifier framework focused on a hybrid adaptive neuro-fuzzy approach to accomplish this goal. In the user query, similarity measurement, and the image content, fuzzy sets represent the vagueness that occurs in such data sets. The optimized classifying method 'hybrid adaptive neuro-fuzzy is enhanced with the improved cuckoo search optimization. Score values are determined by utilizing the linear discriminant analysis (LDA) of such classified images. The preliminary findings indicate that the proposed approach can be more reliable and effective at estimation than can existing approaches.

Detection of lung tumor using dual tree complex wavelet transform and co‐active adaptive neuro fuzzy inference system classification approach

AbstractThe automatic detection and location of the tumor regions in lung images is more important to provide timely medical treatments to patients in order to save their lives. In this article, machine learning‐based lung tumor detection, classification and segmentation algorithm is proposed. The tumor classification phase first smooth the source lung computed tomography image using adaptive median filter and then discrete time complex wavelet transform (DT‐CWT) is applied on this smoothed lung image to decompose the entire image into a number of sub‐bands. Along with the decomposed sub‐bands, DWT, pattern, and co‐occurrence features are computed and classified using co‐active adaptive neuro fuzzy inference system (CANFIS). The tumor segmentation phase uses morphological functions on this classified abnormal lung image to locate the tumor regions. The multi‐evaluation parameters are used to evaluate the proposed method. This method is compared with the other state‐of‐the‐art methods on the same lung image from open‐access dataset.

Power Quality Classification of disturbances using Discrete Wavelet Packet Transform (DWPT) with Adaptive Neuro-Fuzzy System

With the development of the technologies, the demand for good quality of electric power is increasing day by day. In Distributed Generation Systems (DGs), the quality of power can cause serious problems such as sensitive equipment's malfunction, the temperature riseof machines. Therefore, detection of power quality events in the power system is more important to take further actions. The existing power quality events classification methods have high computational time with low accuracy. In order to overcome this problem, this paper presents Discrete Packet Wavelet Transform-Kalman filter based Adaptive Neuro-Fuzzy approach for identification and classification of PQ events. The proposed method classifies the events with better classification accuracy, less convergence time and low in error prediction. The results show that the proposed method has better performance compared with the existing classification methods. The proposed method is Implemented and tested using MATLAB and it provides more accuracy when compared to the existing systems such as Discrete Wavelet Transform based Fuzzy Logic Adaptive System and Fourier Transform based Artificial neural networks etc..

Adaptive neuro-fuzzy approach for prediction of global solar radiation for 25 cities falling under seven Köppen climatic zones

Estimation of solar energy is essential for the identification of suitable locations for solar-based energy systems and their optimal sizing. In this work, capability of adaptive neuro-fuzzy infere...

Adaptive neuro-fuzzy approach for prediction of global solar radiation for twenty five cities falling under seven K

Adaptive neuro-fuzzy approach for prediction of global solar radiation for twenty five cities falling under seven K

Adaptive neuro-fuzzy approach for prediction of dewpoint pressure for gas condensate reservoirs

Dewpoint pressure is an important parameter for reservoir management and characterization. Gas condensate reservoirs experience significant reduction in productivity when initial reservoir pressure decreases below the dewpoint pressure. As such, an effective and efficient methods for prediction of this thermodynamic quantity are crucial for operational plans. In this article, a hybrid artificial intelligence model, based on adaptive neuro-fuzzy approach, for the prediction of gas condensate dewpoint pressure is presented. The proposed model combines the learning ability of artificial neural network and the capability of rule-based fuzzy inference system. First, fuzzy subtractive clustering technique is applied to a set of measured input/output data to identify an initial system based on extracted set of rules. The generated system is then trained using adaptive neuro-fuzzy inference system after which model validation and testing were performed. The performance of the proposed model is compared with existing methods. The results show that our proposed model outperforms the previous and existing methods with 99% accuracy and with the least root mean square error of 2.188 for some selected fluid sample.

Adaptive neuro-fuzzy approach to sodium chlorate cell modeling to predict cell pH for energy-efficient chlorate production

Sodium chlorate production is a highly energy-intensive electro-chemical process. The current efficiency is as low as 66.66% if the pH of the process is not controlled. Hence for energy-efficient sodium chlorate production, it is very important to predict the pH of the cell electrolyte. Though the analytical model of the chlorate cell is available, they are not suitable for pH prediction, as it requires a large number of parameters, which are not easily measurable. This study presents an adaptive neuro-fuzzy inference system model for predicting the pH of sodium chlorate cell from real plant data. This model uses fewer and easily measurable parameters for modeling such as flow rates of brine, HCl, NaOH, cell electrolyte temperature, DC load current, and pH of feed input. An analysis of the accuracy of six models with different combinations of these input parameters is conducted with different model settings for the type of membership functions, epoch number, and number of data sets. The model accuracy is evaluated and compared for six models using the statistical indicators, root mean square error (RMSE), and coefficient of determination (R2). Based on the results obtained, the ANFIS model with HCl flow rate, NaOH flow rate, cell electrolyte temperature, DC load current, and pH of feed as input parameter, is accurate in predicting pH with RMSE 0.1290 and R2 0.8816 at testing stage. The good agreement between the measured pH and estimated pH confirms that the model can be further used to design the controller.

Online Medical Data Stream Mining Based on Adaptive Neuro-Fuzzy Approaches

Online Medical Data Stream Mining Based on Adaptive Neuro-Fuzzy Approaches