Showing papers on "Neuro-fuzzy published in 2022"

The Fusion of Deep Learning and Fuzzy Systems: A State-of-the-Art Survey

Abstract: Deep learning presents excellent learning ability in constructing learning model and greatly promotes the development of artificial intelligence, but its conventional models cannot handle uncertain or imprecise circumstances. Fuzzy systems, can not only depict uncertain and vague concepts widely existing in the real world, but also improve the prediction accuracy in deep learning models. Thus, it is important and necessary to go through the recent contributions about the fusion of deep learning and fuzzy systems. At first, we introduce the deep learning into fuzzy community from two perspectives: statistical results of relevant publications and conventional deep learning algorithms. Then, the fusing framework and graphic form of deep learning and fuzzy systems are constructed. Followed by, are the current situations of several types of fuzzy techniques used in deep learning, some reasons why use fuzzy techniques in deep learning, and the application fields of the fusion, respectively. Finally, some discussions and future challenges are provided regarding the fusion technology of deep learning and fuzzy systems, the application scenarios of fusing deep learning and fuzzy systems, and some limitations of the current fusion, respectively. After summarizing the recent contributions, we have found that this field is an emerging research direction and it is increasingly paying much more attention. Especially, fuzzy systems make great effects on deep learning models in the aspect of classification, prediction, natural language processing, auto-control, etc., and the fusion is applied into different fields, like but not limited to computer science, natural language, medical system, smart energy management systems and manufacturing industry.

Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression

Abstract: As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: determining fuzzy sets is not robust to data sequence; determining fuzzy rules is complex as subspaces that can approximate to a Takagi–Sugeno–Kang (TSK) rule need to be obtained, and many parameters need to be optimized; and it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this article, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules; instead uses a simple inference that considers fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.

Building and training a fuzzy neural model of data mining tasks

Abstract: In this paper discussed the problem of constructing and training a fuzzy neural network based on fuzzy logical rules. Based on the constructed model, the developed algorithm, the objects classified with indistinct initial information. Under these conditions, traditional methods of mathematical statistics or simulation modeling do not allow building adequate models for solving data mining problems. We consider the solution of problems based on the use of intelligent modeling technologies using methods of neural networks and fuzzy logic in order to automate the construction of fuzzy rules based on methods and algorithms of machine learning.

A Self-Organized Method for a Hierarchical Fuzzy Logic System Based on a Fuzzy Autoencoder

Abstract: In this article, a novel design of a hierarchicalfuzzy system (HFS) based on a self-organized fuzzy partition and fuzzy autoencoder is proposed. The initial rule set of the system is empty, and all the fuzzy sets and fuzzy rules are generated by a self-organized fuzzy partition algorithm. By adopting an improved box plot data standardization method, the processed data can more accurately represent the distribution characteristics of the input data, which improve the accuracy and the rationality. A fuzzy autoencoder is used to train the HFS layer by layer, which can not only ensure the effectiveness of the fuzzy system's hidden layer variables but also provide interpretability. Compared with the traditional fuzzy logic system, the HFS reduces the total number of rules and the complexity. The proposed HFS is tested on three different regression datasets. The experimental results illustrate that the hierarchical self-organized fuzzy system still performs better in terms of regression accuracy indicators than the self-organized fuzzy system.

Vibration-Based Fault Detection in Drone Using Artificial Intelligence

Abstract: Recent years have seen a huge increase in the study of drones. There is a lot of published articles regarding drone, focusing on control optimization, fault detection, safety mechanisms, etc. In fault detection, most studies focused on the effects of faulty propellers and rotors, and there is very limited academic research on drone arms. In this paper, a fault detection based on the vibration of the multirotor arms using artificial intelligence (AI) is proposed. There are some cases in which, due to accident, the arm of the multirotor crack or loosen. This is normally unnoticeable without disassembly, and if not taken care of, it would have likely resulted in a sudden loss of flight stability, which will lead to a crash. Different types of AI methods are incorporated in this study, namely, fuzzy logic, neuro-fuzzy, and neural network (NN). Their results are compared to determine the best method in predicting the safety of the multirotor. Fuzzy logic and neuro-fuzzy methods provided acceptable decision-making, but the performance of the neuro-fuzzy approach depend on the dataset used because overfit model might give incorrect decision-making. This also applies to the NN technique. Because the vibration data are collected in the laboratory environment without consideration of wind effect, this framework is more suitable for early prediction before flying the multirotor in the outdoor environment.

Application of neuro-fuzzy system for predicting the success of a company in public procurement

Abstract: The paper presents a neuro-fuzzy system for evaluating and predicting the success of a construction company in public tenders. This model enables companies to operate sustainably by assessing their own position in the market. The model was based on data from a seven-year study, where data from the first six years were used to adjust the model, while data from the last year of the study were used for testing and validation. The neuro-fuzzy model was tuned using the Artificial Bee Colony algorithm.

Interpretable Mamdani neuro-fuzzy model through context awareness and linguistic adaptation

Abstract: Interpretable machine learning is trending as it aims to build a human-understandable decision process. There are two main types of machine learning systems: white-box and black-box models. White-box models are inherently interpretable but commonly suffer from under-fitting phenomena; on the other hand, black-box models perform quite well in a wide range of application domain problems, but their reasoning behind a decision is hard or even impossible to understand. In the soft-computing area, fuzzy inference systems are rule-based systems that use fuzzy reasoning, bringing human perception modeling and computing with word capability. These rule-based systems are designed either manually or automatically but are commonly optimized to fit better some phenomena’ data (in a supervised learning task). After the optimization process, the initial semantic meaning of fuzzy sets is modified (slightly, in the best cases), creating a gray-box model. The principal objective of the proposed methodology in this paper is to extract a high-quality rule in terms of comprehensibility, accuracy and fidelity. This is accomplished by using a fuzzy linguistic interpretable model from an optimized neuro-fuzzy model, considering the initial knowledge context with which it was built. A grammar-guided genetic algorithm is used as the optimization process to find the interpretable description of the model. A collection of 16 datasets for classification tasks were used to evaluate our proposal, obtaining an f1-score of 0.814 with 0.026 standard deviation in the optimized model; the obtained fidelity, in terms of similarity from the interpretable model to the optimized one, was 0.93 of mean with 0.018 standard deviation. Obtained results show that neuro-fuzzy systems could play an important role in interpretable machine learning, providing natural language explanations from previous knowledge.

Intelligent Medical Diagnostic System for Hepatitis B

Abstract: The hepatitis B virus is the most deadly virus, which significantly affects the human liver. The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage; otherwise, it will become a severe problem and make a human liver suffer from the most dangerous diseases, such as liver cancer. In this paper, two medical diagnostic systems are developed for the diagnosis of this life-threatening virus. The methodologies used to develop these models are fuzzy logic and the neuro-fuzzy technique. The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developed models. The classification accuracy of a multilayered fuzzy inference system is 94%. The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System (ANFIS) classifies the result corresponding to the given input is 95.55%. The comparison of both developed models on the basis of their performance parameters has been made. It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system. Furthermore, the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease. In other words, the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems

Abstract: Developing accurate models for photovoltaic (PV) systems has a significant impact on the evaluation of the accuracy and testing of PV systems. Artificial intelligence (AI) is the science of developing machine jobs to be more intelligent, similar to the human brain. Involving AI techniques in modeling has a significant modification in the accuracy of the developed models. In this paper, a novel dynamic PV model based on AI is proposed. The proposed dynamic PV model was designed based on an adaptive neuro-fuzzy inference system (ANFIS). ANFIS is a combination of a neural network and a fuzzy system; thus, it has the advantages of both techniques. The design process is well discussed. Several types of membership functions, different numbers of training, and different numbers of membership functions are tested via MATLAB simulations until the AI requirements of the ANFIS model are satisfied. The obtained model is evaluated by comparing the model accuracy with the classical dynamic models proposed in the literature. The root mean square error (RMSE) of the real PV system output current is compared with the output current of the proposed PV model. The ANFIS model is trained based on input–output data captured from a real PV system under specified irradiance and temperature conditions. The proposed model is compared with classical dynamic PV models such as the integral-order model (IOM) and fractional-order model (FOM), which have been proposed in the literature. The use of ANFIS to model dynamic PV systems achieves an accurate dynamic PV model in comparison with the classical dynamic IOM and FOM.

Fuzzy logic and fuzzy systems

Abstract: A popular way to model knowledge in computers is by means of using IF-THEN rules. Fuzzy logic allows a straightforward manner to model them by using words instead of crisp values. This makes it more similar to human reasoning, which does not need precise rules for allowing the decision-making process in most of our daily life activities. Also, as it can be inferred, this kind of rules could be directly taken from experts (clinicians), looking for developing tools of computer-assisted diagnostic, whose final goal is not the replacement of the clinicians, but provide them of a support. In this chapter, the main fundamentals related to fuzzy logic are presented: logic operations, fuzzy inference systems, and fuzzy relations. Furthermore, they are also linked to more powerful hybrid systems, such as neuro-fuzzy models. Specifically, this could guide the reader to understand, design, integrate, and implement these concepts into novel hybrid systems.

Self-Organizing Interval Type-2 Fuzzy Neural Network Using Information Aggregation Method

Abstract: Interval type-2 fuzzy neural networks (IT2FNNs) usually stack adequate fuzzy rules to identify nonlinear systems with high-dimensional inputs, which may result in an explosion of fuzzy rules. To cope with this problem, a self-organizing IT2FNN, based on the information aggregation method (IA-SOIT2FNN), is developed to avoid the explosion of fuzzy rules in this article. First, a relation-aware strategy is proposed to construct rotatable type-2 fuzzy rules (RT2FRs). This strategy uses the individual RT2FR, instead of multiple standard fuzzy rules, to interpret interactive features of high-dimensional inputs. Second, a comprehensive information evaluation mechanism, associated with the interval information and rotation information of RT2FR, is developed to direct the structural adjustment of IA-SOIT2FNN. This mechanism can achieve a compact structure of IA-SOIT2FNN by growing and pruning RT2FRs. Third, a multicriteria-based optimization algorithm is designed to optimize the parameters of IA-SOIT2FNN. The algorithm can simultaneously update the rotatable parameters and the conventional parameters of RT2FR, and further maintain the accuracy of IA-SOIT2FNN. Finally, the experiments showcase that the proposed IA-SOIT2FNN can compete with the state-of-the-art approaches in terms of identification performance.

Research on Robot Fuzzy Neural Network Motion System Based on Artificial Intelligence

Abstract: An intelligent controller based on a self-learning interval type-II fuzzy neural network is proposed to make the motion controller of the industrial intelligent robot with good adaptability. This controller has a parallel structure and contains an interval type-II fuzzy neural network and a conventional PD controller. For the design of the interval type-II fuzzy neural network, the interval type-II fuzzy set is established using the slave design method. In the design process of the interval type-II fuzzy set of the front piece, a dual sequence symmetric trapezoidal subordinate function arrangement method is proposed, which makes the self-learning law and stability analysis of the system in an analytic form and facilitates the implementation of the algorithm in hardware. In the design of the neural network self-learning law, a parametric self-learning algorithm based on sliding mode control theory is established to adjust the structural parameters of the interval type-II fuzzy neural network online, and the stability of the system is proved by using Lyapunov's stability theorem. Three sets of validation simulation experiments are given in conjunction with the trajectory tracking problem of the Delta parallel robot. The simulation results show that, in the presence of system uncertainty, the intelligent controller based on interval self-learning interval type-II fuzzy neural network can significantly improve the trajectory tracking accuracy and robustness of the system and make the control system highly adaptable to the environment. Experiments of intelligent control system based on self-learning interval type-II fuzzy neural network and experiments of reusable particle swarm optimal motion planning method are designed, and the effectiveness of the intelligent control system and motion planning method is verified on the experimental platform. The experimental results show that the intelligent control system based on the self-learning interval type-II fuzzy neural network can effectively improve the accuracy and stability of robot trajectory tracking control, and the reusable particle swarm optimal motion planning method can quickly solve the robot motion planning problem with complex constraints online.

Neural network operators of fuzzy n-cell number valued functions and multidimensional fuzzy inference system

Abstract: In this paper, we introduce a novel family of neural network operators of fuzzy n-cell number valued functions, activated by a collection of multivariate sigmoidal functions. We give some special examples of these activation functions with graphs and present some illustrative examples to demonstrate the approximation performance of these operators. Moreover, we propose a multidimensional fuzzy inference system including neural network operators of fuzzy n-cell number valued functions for the symptom-based diagnosis of Covid-19 disease. Finally, we give some approximation results using an Lp type metric of fuzzy n-cell numbers and examine the rate of convergence for the operators by means of fuzzy Lp modulus of continuity.