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In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

The rapidly changing climate affects an extensive spectrum of human-centered environments. The food industry is one of the affected industries due to rapid climate change. Rice is a staple food and an important cultural key point for Japanese people. As Japan is a country in which natural disasters continuously occur, using aged seeds for cultivation has become a regular practice. It is a well-known truth that seed quality and age highly impact germination rate and successful cultivation. However, a considerable research gap exists in the identification of seeds according to age. Hence, this study aims to implement a machine-learning model to identify Japanese rice seeds according to their age. Since agewise datasets are unavailable in the literature, this research implements a novel rice seed dataset with six rice varieties and three age variations. The rice seed dataset was created using a combination of RGB images. Image features were extracted using six feature descriptors. The proposed algorithm used in this study is called Cascaded-ANFIS. A novel structure for this algorithm is proposed in this work, combining several gradient-boosting algorithms such as XGBoost, CatBoost, and LightGBM. The classification was conducted in two steps. First, the seed variety was identified. Then, the age was predicted. As a result, seven classification models were implemented. The performance of the proposed algorithm was evaluated against 13 state-of-the-art algorithms. Overall, the proposed algorithm has a higher accuracy, precision, recall, and F1-score than the others. For the classification of variety, the proposed algorithm scored 0.7697, 0.7949, 0.7707, and 0.7862, respectively. The results of this study confirm that the proposed algorithm can be employed in the successful age classification of seeds.

Most time series with a clear overall trend in their data and graphs require a model that effectively addresses the overall trend. If the time series also includes various fluctuations and random variations, nonlinear models are the ideal approach. To improve the prediction error and make it very small, a new model was applied to the time series of annual cancer cases in Iraq for the period from 1976 to 2023. This series contains a general trend covering more than 85% of the data, in addition to various random fluctuations and variations. The proposed hybrid model consists of two parts: the first part addresses the strong overall trend in a linear manner by partitioning the series into an optimal number of parts according to the optimal division that gives the lowest value for RMSE and MAPE, and applying a double exponential smoothing method to all parts to address the upward trend. The second part detects nonlinear patterns in the residuals of the first model using an adaptive network-based fuzzy inference system (ANFIS). The proposed hybrid model, Partitioned Double Exponential Smoothing (PDES-ANFIS), has proven to be more efficient compared to the unpartitioned hybrid model and single models by using the root mean square error (RMSE).

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons.

Automated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.

The implementation of soft computing procedures in tool wear prediction and optimization is a significant process in machining operations for sustainable manufacturing of components with quality finishing. Tool wear is one of the response parameters that leads to a high rate of production cost due to constant tool substitution during machining, mostly when machining hard metals that are difficult to machine. With these challenges, several techniques have been put in place to optimize and predict tool wear rates, including turning, milling, grinding, shaping, and drilling. This study focuses on the evaluation of existing literature that employs soft computing procedures such as ANN-GA, ANFIS, ANFIS-PSO, and ANFIS-FCM in the prediction of cutting tool wear rate during machining processes. From the different study reviews, the results show that the application of these soft computing procedures significantly improves tool life during the manufacturing process by employing the optimal machining parameters in an eco-friendly nano-lubrication environment. This study also points out the challenges currently faced with these soft computing techniques and gives a sustainable way forward as a recommendation to improve the manufacturing process.

In this paper, a control strategy for a non-holonomic robot based on an Adaptive Neural Fuzzy Inference System is proposed. The neuro-controller makes it possible for the robot to track a given reference trajectory. After a short introduction about Adaptive Neural Fuzzy Inference System, the control strategy which is used on our virtual non-holonomic robot is described. And finally, the simulationsâ results where the robot has to pass into a narrow path and also the first validation results concerning the implementation of the proposed concepts on a real robot is given.

The purpose of this paper is to design an adaptive neuro-fuzzy controller to control the speed of the BLDC motor. This paper throws overall view of the performance of fuzzy PID controller and compares with fuzzy - adaptive neural controller. Take characteristics of suitable in PID controller is difficult. But fuzzy is the ability to take appropriate control parameters and calculations easier. An adaptive neuro fuzzy control system has the advantages of both types of fuzzy control system and neuro system. Paper examines the BLDC motor speed control based on adaptive neuro - fuzzy First tried to have a simulated fuzzy PID controller And then designing controller adaptive neuro - fuzzy using ANFIS toolbox for the motor. And we compare the characteristics of speed, torque, current and voltage in the three controllers finally, we conclude that the characteristic of the controller adaptive neuro - fuzzy (ANFIS) is better than the other two controllers.

ANFIS-type algorithms have been used in various modeling and simulation problems. With the help of algorithms with more accuracy and adaptability, it is possible to obtain better real-life emulating models. A critical environmental problem is the discharge of saline industrial effluents in the form of buoyant jets into water bodies. Given the potentially harmful effects of the discharge effluents from desalination plants on the marine environment and the coastal ecosystem, minimizing such an effect is crucial. Hence, it is important to design the outfall system properly to reduce these impacts. To the best of the authors’ knowledge, a study that formulates the effluent discharge to find an optimum numerical model under the conditions considered here using AI methods has not been completed before. In this study, submerged discharges, specifically, negatively buoyant jets are modeled. The objective of this study is to compare various artificial intelligence algorithms along with multivariate regression models to find the best fit model emulating effluent discharge and determine the model with less computational time. This is achieved by training and testing the Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS-Genetic Algorithm (GA), ANFIS-Particle Swarm Optimization (PSO) and ANFIS-Firefly Algorithm (FFA) models with input parameters, which are obtained by using the realizable k-ε turbulence model, and simulated parameters, which are obtained after modeling the turbulent jet using the OpenFOAM simulation platform. A comparison of the realizable k-ε turbulence model outputs and AI algorithms’ outputs is conducted in this study. Statistical parameters such as least error, coefficient of determination (R2), Mean Absolute Error (MAE), and Average Absolute Deviation (AED) are measured to evaluate the performance of the models. In this work, it is found that ANFIS-PSO performs better compared to the other four models and the multivariate regression model. It is shown that this model provides better R2, MAE, and AED, however, the non-hybrid ANFIS model provides reasonably acceptable results with lower computational costs. The results of the study demonstrate an error of 6.908% as the best-case scenario in the AI models.