An IDS-based Adaptive Neural Fuzzy Inference System (ANFIS) for IoBT Security Utilizing Particle Swarm Optimization

This work focuses on the correlations among water quality parameters such as total hardness, total dissolved solids, electrical conductivity and total alkalinity with water parameters pH, temperature and the sum of mill equivalents of cations and anions in water reservoir in Kermanshah province (located in the middle of the western part of Iran). The data of water quality of monitoring sites were collected over year 2015. To predict and simulate water quality parameters, two data-driven models, i.e., adaptive neural fuzzy inference system and a hybrid adaptive neural fuzzy inference system structure trained by particle swarm optimization technique, were used. The main advantages of these methods are their high accuracy and very fast computational speed to predict unknown data. The results indicated that implementation of two models is highly satisfactory for predicting inorganic indicators of water quality. However, the flexibility of particle swarm optimization–adaptive neural fuzzy inference system method in modeling is better than adaptive neural fuzzy inference system approach. To prove this, the correlation coefficient, mean absolute error, root mean square error and t statistics were calculated as the error criterion. The overall (training and testing) mean relative error percentage, mean absolute error, root mean square error, correlation coefficient and t statistics obtained by the proposed particle swarm optimization–adaptive neural fuzzy inference system model are less than 3.50, 11.60, 18.90, 0.95 and 0.38%, respectively. The results provide a useful approach that uses water parameters to estimate water quality in water reservoir for water treatment and pollution management.

Supervised learning is mainly used to optimize Adaptive Neural Fuzzy Inference System (ANFIS) controllers. In order to generate data for supervised learning, a controller is designed and optimized using Particle Swarm Optimization (PSO) or any other algorithms. This paper proposes and compares reinforcement learning based ANFIS and Approximate Reasoning Intelligent controller (ARIC) controllers. Reinforcement learning based ANFIS reduces the work flow required to train it by directly optimizing the membership functions using Proximal Policy Optimization (PPO) algorithm. ANFIS and ARIC neuro fuzzy controllers are designed for nonlinear dynamics of coffee roasting process using Schwartzberg’s model. A custom layer is designed for every membership function and fuzzy inference operations using MATLAB’s Deep Learning Toolbox. This neural connectionist model of ANFIS and ARIC is used as actor. The critic which evaluates the goodness of action taken is a two-layer neural network with sigmoidal activation function. Simulink environment is also created to represent the dynamics of coffee roasting process. The agent is trained to track roast profile for 50 episodes. The training converged at 50th iteration. After training, the Root Mean Square Error (RMSE) for ARIC architecture reduced from 0.5134 to 0.08122. Similarly, the RMSE of ANFIS improved from 0.2026 to 0.0624.

Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms

Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach

The operation of the modern electric power system is usually associated with various operational planning procedures. The power system needs to meet the specified technical performance standards and economic performance standards so as to meet the power load. A short-term load forecasting model based on adaptive neural fuzzy inference system is established. Based on Sugeno-type fuzzy neural network model, three kinds of output parameters optimization algorithm (Kaczmarz algorithm, Gradient algorithm and Kalman algorithm) are adopted. The simulation experiments results show the adaptive neural fuzzy inference system based on the Kalman method has the optimal forecasting precision on the short-term load for the power system.

With the wide application of batteries, the battery state of health (SOH) is increasingly concerned by people. The battery SOH reflects its ability to store charge and plays a key role in electric power systems. This paper proposes an adaptive neural fuzzy inference system (ANFIS) based method to estimate the battery SOH. The main steps of the proposed method include model creating, model training and SOH estimation. The constant current charging time, the voltage drop at the beginning of discharge and the released energy within a certain depth of discharge are used as the inputs for the model training. Since the basic ANFIS algorithm has large estimation errors and the model trains slowly, A Fletcher-Reeves based method is proposed to improve the basic ANFIS. The trained model is then used for the estimation of the SOH. Experimental results show that the Fletcher-Reeves based ANFIS method is effective and efficient.

The inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANFIS configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.

When localizing wireless sensor networks, estimating the distances of sensor nodes according to the known locations of the anchor nodes remains a challenge. As nodes may transfer from one place to another, a localization technique that can measure or determine the location of a mobile node is necessary. In this paper, the distance between a bicycle when moves on the cycling track and a coordinator node (i.e., coach), which positioned on the middle of the cycling field was estimated for the indoor and outdoor velodromes. The distance was determined based on two methods. First, the raw estimate is done by using the log-normal shadowing model (LNSM) and later, the intelligence technique, based on adaptive neural fuzzy inference system (ANFIS) is applied to improve the distance estimation accuracy, especially in an indoor environment, which the signal is severely dominated by the effect of wireless multipath impairments. The received signal strength indicator from anchor nodes based on ZigBee wireless protocol are employed as inputs to the ANFIS and LNSM. In addition, the parameters of the propagation channel, such as standard deviation and path loss exponent were measured. The results shown that the distance estimation accuracy was improved by 84% and 99% for indoor and outdoor velodromes, respectively, after applying the ANFIS optimization, relative to the rough estimate by the LNSM method. Moreover, the proposed ANFIS technique outperforms the previous studies in terms of errors of estimated distance with minimal mean absolute error of 0.023 m (outdoor velodrome) and 0.283 m (indoor velodrome).

This study investigated the accurate prediction of the calorific value of municipal solid waste (MSW) using soft computing systems, namely artificial neural networks (ANN), adaptive neural fuzzy inference system (ANFIS), support vector machine (SVM), and multi-layer perceptron (MLP). Calorific value of MSW is a crucial factor that exhibits the energy content of MSW, however, determining calorific value from conventional laboratory methods is quite expensive and difficult. The research focused on proximate analysis parameters obtained from the laboratory and utilized the measured calorific value to develop predictive models. All the models demonstrated a very good correlation between input and output, with consistently strong values of the coefficient of determination (R2). ANN SVM, MLP, and ANFIS models have respective R2 values of 0.9397, 0.8195, 0.7212, and 0.9979. ANFIS showed the best correlation with exceptional predictive power. Statistical parameters were determined to compare model accuracy, with ANFIS exhibiting the top performance, followed by ANN, and then MLP, which had the lowest values of mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE) at 8.704E-07, 0.00019, 0.00016, and 1.295E-05 respectively. However, SVM has the least capability to predict calorific values accurately compared to other models. Soft computing Models, particularly ANFIS, demonstrated remarkable accuracy in predicting the calorific value.

A novel diagnosis method based on optimized subtraction clustering ANFIS (adaptive neural fuzzy inference system) algorithm is proposed in order to improve the accuracy and reliability of diesel engine fault diagnosis. The improved analytic hierarchy process (AHP) and subtractive clustering algorithm are combined to form a new ANFIS network suitable for multi information fusion diagnosis. The initial clustering centers of subtractive clustering algorithm and reasoning rules of ANFIS are automatically optimized by AHP algorithm without relying on expert experience. The effectiveness of the novel algorithm is investigated on the example of multi information fusion diagnosis of diesel engine, and the results indicate that the proposed method can eliminate the disadvantages of more inference rules, slow convergence speed and low diagnostic accuracy of the conventional ANFIS algorithm under multiple input parameters, which means this new method can effectively improve the accuracy of diesel engine fault diagnosis with the advantages of more fusion parameters and less calculation.

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%.

The aim of this study is to design adaptive neural-fuzzy inference system (ANFIS) model and fuzzy expert system for determination of concrete mix designs and finally compare their results. Idea of these systems based on two surveys: first, ACI structures and principles, second a concrete mix designs dataset that collected via Prof. I-Cheng Yeh. Datasets that loaded in to ANFIS has 552 mix designs and based on ACI mix designs. Moreover, in this study, we have designed fuzzy expert system. Input fields of fuzzy expert system are Slump, Maximum Size of Aggregate (Dmax), Concrete Compressive Strength (CCS), and Fineness Modulus. Output fields are quantities of water, cement, fine aggregate (F.A.) and coarse aggregate (C.A.). In the ANFIS model, we have four layers (four ANFIS models): the first layer takes values of Dmax and Slump and then determines the quantity of Water, the second layer takes values of Water (computed in the past layer) and CCS then measures the value of Cement, the third layer takes values of Dmax and Slump to compute C.A. and the fourth layer takes values of Water, Cement, and C.A. (determined in past layers) and then measures the value of F.A. When these systems were designed and tested, comparison between two systems (FIS and ANFIS) results showed that results of ANFIS model are better than fuzzy expert system’s results. In the ANFIS model, for Water output field, training and average testing errors are 0.86 and 0.8. For cement field, training error and average testing error are in the orders of 0.21 and 0.22. Training and average testing error of C.A. are in the orders of 0.0001 and 0.0004 and finally, training and average testing errors of F.A. are in the orders of 0.0049 and 0.0063. Results of fuzzy expert system in comparison to ACI results follow average errors: average error of Water, Cement, C.A., and F.A. are in the orders of 9.5%, 27.6%, 96.5%, and 49%.

This paper presents Artificial Neural Networks (ANN) and Adaptive Neural-Fuzzy Inference System (ANFIS) for reduction of torque and flux ripples in transient and steady state response of Direct Torque Control (DTC) for Induction Motor drive. The Flux and Electromagnetic torque can be controlled by using efficient Direct Torque Control (DTC) scheme This proposed technique is to improve the torque, speed and flux response with the Artificial Neural Network (ANN) and then with the Adaptive Neuro-Fuzzy Inference (ANFIS). This paper shows implementation of DTC system using ANN and ANFIS on three phase induction motor to optimize the flux and to improve the performance of fast stator flux response in transient state. To improve the performance of DTC with the modern technique using ANN and ANFIS approach is implemented and performance of ANN DTC compared with CDTC and ANN DTC with ANFIS is done, conclusion is about the ANN approach shows the better performance than CDTC and ANFIS shows superior performance than ANN. The performance has been tested by using MATLAB/SIMULINK and NEURAL NETWORK toolbox.

To optimize the flow stability and improve application accuracy of the PWM intermittent variable-rate spraying system, which suffers from insufficient flow stability and response delays during changes in travel speed, this study proposes an intelligent control method based on an improved Adaptive Neural Fuzzy Inference System (ANFIS). Flow characteristic data of the solenoid valve were collected under four pressure conditions (0.2–0.5 MPa), drive frequencies (5–20 Hz), and duty cycles (10–90%) using an indoor test system. An ANFIS controller architecture was constructed with target flow rate and actual travel speed as input variables and PWM frequency-duty cycle combinations as output variables. This controller enhances the traditional single-output mode of ANFIS by achieving multi-output collaborative optimization through shared premise parameters, thereby strengthening the system’s nonlinear modeling and control capabilities. To validate the system’s practical performance, a field simulation test platform based on a spraying robot was constructed. By analyzing preset prescription map information, the system achieved precise variable-rate spraying operations during movement. Test results demonstrate that the steady-state error remains within 5.03% under various speed-varying conditions. This research provides a high-precision intelligent control solution for variable-rate spraying systems, holding significant implications for reducing pesticide application rates and advancing precision agriculture.

Machine learning and artificial intelligence techniques are more and more in our lives and studies in this field are increasing day by day. Data is vital for these studies. In order to draw meaningful conclusions from the available data, new methods are proposed and successful results are obtained. The preparation of the obtained data is very important in the studies to be carried out. Data preprocessing is very important in the preparation of data. The most critical stage of the data preprocessing process is the scaling or normalization of the data. Machine learning libraries such as scikit-learn and programming languages such as R provide the necessary libraries to scale data. However, it is not known exactly which normalization method will be applied and which will yield more successful results. The success of these normalization methods has been investigated on many different methods, but such a study has not been done on the adaptive neural fuzzy inference system (ANFIS). The aim of this study is to examine the success of normalization methods on ANFIS in terms of both classification and regression problems. So, for studies using the Anfis method, guidance will be provided on which normalization process will give better results in the data preprocessing stage. Four different normalization methods in the scikit-learn library were applied on the Diabets and Forestfire datasets in the UCI database. The results are presented separately for both classification and regression. It has been determined that min-max normalization in classification problems and working with original data in regression problems are more successful.