Fuzzy Neural Network Model Research Articles

Fuzzy Neural Network (FNN) Pada Proses Identifikasi Penyakit ISPA

ISPA is a disease that can affect anyone from children, adolescents, adults, and even the elderly. The causes experienced by sufferers of this disease are quite simple, such as fever, runny nose, and cough. The discussion in this paper describes the process of ISPA disease identification by developing a Fuzzy Neural Network (FNN) model. The process will be optimized using Fuzzy Logic to form rules for the diagnostic process, then proceed with an Artificial Neural Network (ANN). This model can maximize the performance of ANN in the identification process so that the output given is quite precise and accurate. The results provided by Fuzzy Logic can describe the clarity of the rules in diagnosis by presenting several rules (rules) that are presented from the Fuzzyfication process to the Defuzzyfication process. The output obtained from the ANN process also shows quite perfect results with an average error value based on MSE of 0.00912 and accuracy value of 91.96%. With these results, it can be stated that the FNN model can be used in the ISPA diagnosis process so that the presentation of this paper aims to provide an alternative in the identification process

Compressive Strength Prediction of Green Ultra-High Performance Concrete (GUHPC) Based on Adaptive Network-Based Fuzzy Inference System

Green ultra-high performance concrete (GUHPC) is considered to be a new generation of construction materialsthat adapt to sustainable development and is gradually being used in the fields of bridge reinforcement, housefacades, and paving.To improve the efficiency of green ultra-high performance concrete in the experimental stageand to save the component material, the prediction of the 28-day compressive strength of green ultra-highperformance concrete has become a challenging task. According to the published literature, the compressivestrength of concrete is closely related to the material composition such as cement, fly ash, silica fume, sand, etc. Soin this study, 175 groups of related data of GUHPC were collected to form a database, and an artificial neuralnetwork system combined with IF-THEN fuzzy rules was utilized to establish a model that could better predict the28-day compressive strength of GUHPC. Three evaluation indicators, RMSE, R2, and MAPE, indicate that theprediction of the compressive strength of green ultra-high performance concrete made by the model is completelyreliable. Overall,this study successfully proposes a fuzzy artificial neural network model for predicting the 28-daycompressive strength of GUHPC, which provides a viable prediction tool for GUHPC in the experimental stage.

An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

Human motion retrieval and analysis is a useful means of activity recognition to 3D human bodies. An efficient method is proposed to estimate human motion by using symmetric joint points and limb features of various limb parts based on regression task. We primarily obtain the 3D coordinates of symmetric joint points based on the located waist and hip points. By introducing three critical feature points on torso and symmetric joint points’ matching on motion video sequences, the 3D coordinates of symmetric joint points and its asymmetric limb features will not be affected by shading and interference of limb on different postures. With the asymmetric limb features of various human parts, a dynamic regulated Fuzzy neural network (DRFNN) is proposed to estimate human motion for different asymmetric postures using learning algorithm of network parameters and weights. Finally, human sequential actions corresponding to different asymmetric postures are presented according to the best retrieval results by DRFNN based on 3D human action database. Experiments show that compared with the traditional adaptive self-organizing fuzzy neural network (SOFNN) model, the proposed algorithm has higher estimation accuracy and better presentation results compared with the existing human motion analysis algorithms.

Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper presents an artificial intelligence and big data analytics-based ICH e-diagnosis (AIBDA-ICH) model using CT images. The presented model utilizes IoMT devices for data acquisition process. The presented AIBDA-ICH model involves graph cut-based segmentation model for identifying the affected regions in the CT images. To manage big data, Hadoop Ecosystem and its elements are mainly used. In addition, capsule network (CapsNet) model is applied as a feature extractor to derive a useful set of feature vectors. Finally, the presented AIBDA-ICH model makes use of the fuzzy deep neural network (FDNN) model to carry out classification process. For validating the superior performance of the AIBDA-ICH method, an extensive set of simulations were performed and the outcomes are examined under diverse aspects. The experimental values pointed out the improved e-diagnostic performance of the AIBDA-ICH model over the other compared methods with the precision and accuracy of 94.96% and 98.59%, respectively.

Forecast and Evaluation of Educational Economic Contribution Based on Fuzzy Neural Network

According to the theory that the degree of education of workers in educational economics has a certain positive relationship with social labor productivity, the fuzzy system and neural network modeling mechanism are used to establish the fuzziness of laborers’ education level to social productivity (per capita national income). This article combines fuzzy theory and neural network theory to construct an empirical model for the analysis of the contribution of education economy and conduct an empirical analysis of statistical data from 2010 to 2020. Analysis shows that there is a great correlation between per capita years of education and per capita GDP, especially the number of college students per million people has a greater correlation with per capita GDP. This fully confirms that economic growth is increasingly dependent on education, especially higher education. The main body of this article is the improvement of the measurement model and the calculation of the contribution of our country’s education to economic growth using the fuzzy neural network measurement model. The final empirical conclusion shows that education has a significant role in promoting the development of our country’s economy.

Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network

The coordinated control systems (CCS) in ultra-supercritical thermal power unit, like many other industrial systems, is a complex multivariable system with severe nonlinearity, strong multivariable coupling and uncertainties. In order to meet the requirements of operational stability, economy. etc in ultra-supercritical unit, it is necessary to establish its accurate mathematical model and further design the advanced controller. Against this background, a new fuzzy neural network modeling method is proposed in this paper. First of all, the incremental model is considered separately to improve the rationality of the local linear model structure. Then, the parameters in antecedent part is initialized by a kernel k-means++ algorithm, in which Xie-Beni index is used to optimize the number of fuzzy rules. Finally, supervised adaptive gradient descent algorithm and artificial immune particle swarm optimization algorithm work in stages to complete the training of the consequent part parameters. The proposed modeling method in this paper is applied to a 1000 MW unit in China and shows satisfactory accuracy. In the established model, the MSE of power output, main steam pressure and separator outlet steam temperature are 0.0099, 1.21E-4, 0.0023, respectively. Both numerical and graphical simulation results confirm the effectiveness of the presented fuzzy neural network in modeling.

Foreign Trade Export Forecast Based on Fuzzy Neural Network

This article first analyzes the significance and methods of foreign trade export forecasting and determines the index system of foreign trade export forecasting by analyzing the results of foreign trade export forecasting research at home and abroad. Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural network and fuzzy theory are combined to establish the prediction model. Finally, according to the characteristics of foreign trade exports, this article comprehensively considers the influence of various factors, applies the fuzzy neural network model to the foreign trade export forecast, introduces the whole process of the establishment of the fuzzy neural network forecasting model in detail, and predicts the change interval of foreign trade exports.

Psychological health analysis based on fuzzy assisted neural network model for sports person

Stress is indeed a life aspect that influences everyone, even though athletes seem to suffer from it one step ahead of others because of the extent they are expected to balance between coursework, workouts, and competitions, along with everyday life and family stress. Therefore, an efficient psychological health analysis for sportspersons is crucial in sports training. This paper introduces a Fuzzy-assisted Neural Network model for Psychological Health Analysis (FNN-PHA) to assess mental stress by monitoring the Electro Cardio Gram signal (ECG), Electroencephalogram (EEG), and Pulse rate. This paper integrates the fuzzy assisted Petri nets, fuzzy assisted k-complex detector, and fuzzy assisted transient time analyzer to ensure the psychological health analysis neural network model’s adaptive performance. The strength of the proposed fuzzy model demonstrates interpretability against the accuracy of different criteria. The simulation analysis shows that the FNN-PHA model enhances the prediction ratio of 98.7%, emotional stability of 96.7%, personal growth of 95.7%, physical fitness level of 97.8%, and depression ratio of 12.5% when compared to other existing models.

An intelligent Bayesian hybrid approach to help autism diagnosis

This paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.

E-Commerce Enterprise Supply Chain Financing Risk Assessment Based on Linked Data Mining and Edge Computing

In recent years, the rapid development of information technology has affected the way the world economy operates. The emergence of e-commerce has greatly shortened the time and space distance between economic participants and maximized the sharing of resources. However, the financial management and risk assessment capabilities of the existing supply are insufficient to adapt to the rapidly developing new environment. This article uses a combination of normative analysis and empirical analysis to analyze the status quo of the supply chain of small and medium e-commerce companies. First, this article establishes an evaluation framework for the supply chain of e-commerce companies based on edge computing. Second, according to the distribution of the supply chain, this article adds the member’s predetermined quota, reputation, execution time, and other indicators as parameters to establish a fuzzy neural network model. On this basis, combined with the price regression model, the pricing plan is evaluated. The results show that the financing risk obtained by this model differs very little from the actual risk. The above-mentioned model constructs an e-commerce enterprise supply chain financing risk management model that adapts to the environment of the new era.

Fuzzy Neural Network-Based Evaluation Algorithm for Ice and Snow Tourism Competitiveness

This paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow tourism, and finally selects more than 40 indicators of the three subsystems of resources, economy, and culture. Based on the construction of cloud fuzzy neural network model, the above method is used for experimental comparison analysis, and experiments are conducted through University of California Irvine (UCI) dataset and engineering examples to compare with the traditional cloud model, fuzzy neural network, and BP neural network to analyze the operation efficiency, accuracy rate, and several rules of the algorithm. Through the experimental comparative analysis, the cloud fuzzy neural network can fully take into account the randomness and fuzziness of the data, optimize the generation of cloud rules, avoid multidimensional rule disasters, and ensure the operational efficiency of the algorithm; the accuracy rate of the algorithm is improved relative to that of the traditional technology, and it applies to a variety of datasets. And the software is used to test the ice and snow tourism industry system dynamics model to realize the correctness and robustness testing of the model. After the constructed model can reflect the real situation within the error range, the final policy simulation of the model is carried out.

Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks

The work investigates neural network model for prediction of post-surgical treatment risks. The descriptors of the risk classifiers are formed on the basis of the analysis of the current-voltage characteristics of one, two and three biologically active points. The training and verification samples were formed by examining 120 patients with a diagnosis of benign prostatic hyperplasia. Of these, 62 patients were successfully operated on (class C1), 30 had various complications after surgery (class C2), 28 patients required additional treatment (class C3). The constructed classifiers showed a high quality of predicting critical conditions during surgical treatment.

Bank Loan Strategy Based on Evaluation and Decision Model

In real life, in the face of small and medium-sized enterprises lacking mortgage assets, banks usually provide loans to enterprises with strong strength and stable supply-demand relationship according to credit policies, transaction notes information of enterprises and influence of upstream and downstream enterprises, and can give preferential interest rate to enterprises with high reputation and small credit risk. In order to better meet the application of the model in real life, the enterprise loan mode is introduced, and the risk assessment of each loan mode is carried out according to the actual situation. Finally, the fuzzy neural network model is introduced. The enterprise loan mode and three secondary indicators are taken as the input of the fuzzy neural network. Finally, a three-level index: loan risk assessment is obtained. Using Python program to preprocess the data, the original indicators in the given materials are extracted as the first level indicators, and then on the basis of the first level indicators, the main factor analysis and other methods are used to analyze the characteristics and scoring of the second level indicators. Then, the entropy weight is established to obtain the higher-level index loan tendency, and the loan propensity and enterprise reputation are used as parameters to establish the decision-making Model, step by step to get the bank’s quota allocation, interest rate, and finally get the bank allocation plan. In order to supplement the unknown data, this paper uses the known information to forecast the whole process of the unknown quantity, and then uses the data to forecast. First of all, it forecasts whether the default occurs, adds the predicted value to the whole eigenvalue, then forecasts the reputation rating, and finally completes the reasonable supplement of location data. TOPSIS model, SPSS software simulation machine learning, Fisher discriminant analysis

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot.

(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.

Leak flow prediction during loss of coolant accidents using deep fuzzy neural networks

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

A weighted fuzzy process neural network model and its application in mixed-process signal classification

A weighted fuzzy process neural network (WFPNN) model, and its corresponding classification algorithm, are proposed for quantitative and qualitative mixed-process signal classification problems. Based on predicate logic, fuzzy process neurons (FPN) are expressed as weighted fuzzy process inference rules on the semantic. Among them, the premise and conclusion are fuzzy predicates defined on a fuzzy set containing process information. Considering the applicability of rules to practical problems, confidence factor and applicable threshold are introduced to make the rules based on FPN have adaptive application ability. Based on FPN, the WFPNN was constructed with a process signal input layer, an FPN hidden layer, and a Takagi-Sugeno fuzzy classifier. It can integrate the learning properties and classification mechanisms of process neural networks (P-NNs) for time-vary signals with the logical inference abilities of fuzzy systems. So then to realize the quantitative and qualitative mixed process signal processing and direct classification. This study proposes a fusion analysis model that combining fuzzy decision theory with the process information processing method of P-NNs. It can automatically deduce the membership function and the fuzzy predicate logic rules from a set of given training examples, quickly build a fuzzy expert system prototype. And it can provide a new method for the construction and application of an expert system based on models or rules. Meanwhile, due to the adaptive learning property of WFPNN and the representation mechanism of inference rules based on the FPN, it is easy to realize the extension of the rule bases, model bases and application fields of the expert systems. This paper analyzes the properties of WFPNN, and establishes a corresponding learning algorithm. The discrimination of reservoir water-flooded states based on well-logging curves was used as an example for experimental analysis. The continuous measurement values of five physical quantities, such as resistivity, acoustic and radioactivity levels in the oil layer, were taken as input. The output was the discriminating result for four types of water-flooded states. The accuracy of the proposed method was 82.5%. Our accuracy was more than 7% higher than other existing automatic identification methods. These results demonstrate the viability and effectiveness of the proposed model and algorithms.

Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters

The global solar irradiance data plays a vital role in evaluating the performance of all the solar energy conversion devices. In general there are two methods to predict the performance of such irradiance, namely physical models and the machine learning models. This paper presents a generalized regression neural network model (a machine learning technique) for estimating the global solar irradiance using seasonal and meteorological factors as input parameters. Results obtained from this proposed generalized regression neural network approach are compared with the results estimated by extensively used machine learning based methodologies such as fuzzy and artificial neural network models. Such a comparative results clearly indicate that prediction accuracy of proposed generalized regression neural network model is in good agreement with experimentally measured values. The mean percentage error for using GRNN, fuzzy logic and artificial neural network are 3.55%, 4.64%, and 5.49%.

Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network.

In recent years, with the gradual development of sports, the competition between athletes is becoming more and more fierce. The long training time and heavy body load of athletes lead to the increase of the incidence of sports injury, and the evaluation and analysis of athletes' sports injury need a lot of manpower and material resources. In order to improve the calculation efficiency of sports injury estimation results and save the cost of estimation and analysis, we propose a sports injury estimation model based on the algorithm model of mutation fuzzy neural network. The sports injury model constructed in this paper can not only systematically evaluate and analyze the degree of sports injury of athletes, but also improve the accuracy and efficiency; at the same time, it has universality for the evaluation and analysis of the degree of sports injury. The construction of this model provides the theoretical basis of big data algorithm for the prevention of sports injury and the application of mutation fuzzy neural network in the field of sports.

IWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called i WorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the i WorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users' proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from an intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employees to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.

Technology for risk assessment at product lifecycle stages using fuzzy logic

The problem of risk assessment at the stages of the product life cycle using both qualitative and quantitative approaches is investigated, and a generalized algorithm for selecting a fuzzy risk assessment model with different input data and system requirements is proposed for the effective use of statistical information and expert assessments. The "risk-based approach" allows to reduce the cost of correcting possible errors in the future and reduce the uncertainty when performing subsequent actions. It is noted that the results of SWOT analysis, as a rule, are of a qualitative descriptive nature, and do not contain specific recommendations. The provisions of modern standards on risk analysis are analyzed and the classification of risk analysis methods is given in accordance with the provisions of the national standard GOST R 58771-2019 "Risk management. Technologies for risk assessment", in which the key is the concept of uncertainty, estimated using different scales of gradation of risk damage and probability of its occurrence. An approach based on fuzzy logic and a hybrid fuzzy neural network model is proposed, which allows to present the used criteria in a con-venient form and implement a logical conclusion using simple and visual production rules. At the same time, the effectiveness and accuracy of the developed risk assessment system based on fuzzy logic is mainly determined by the quality of expert information and the consistency of the methods used to obtain it. To improve the accuracy of the results, it is proposed to use collective expert estimates with subsequent analysis of the consistency of the obtained expert estimates by determining the coefficients of variation, rank correlation, concordation, and so on. A generalized algorithm of expert assessment is presented, which is recommended to follow when developing expert systems for risk analysis. Various models of fuzzy inference (Mamdani, Takagi-Sugeno, hybrid neuro-fuzzy inference) are considered. An algorithm for constructing a fuzzy risk analysis system based on an effective method for obtaining expert assessments and analyzing statistical information is proposed. It is suggested that if there is a priori information about previously occurred events that can be used for risk analysis and fore casting, the fuzzy conclusion should be refined using widely known methods of mathematical statistics, optimization algorithms, for example, gradient descent, simplex method or genetic algorithms. An example of developing a risk assessment system when an enterprise enters into contracts with both the customer and co-executors is given.