Intuitionistic Fuzzy Neural Network Research Articles

Event-triggered model-free adaptive control for nonlinear systems using intuitionistic fuzzy neural network: simulation and experimental validation

This article presents model-free adaptive control based on an intuitionistic fuzzy neural network for nonlinear systems with event-triggered output. Essentially, model-free adaptive control (MFAC) is constructed by establishing an online approximate model of the controlled system using the pseudo-partial derivative (PPD) form. By the proposed scheme, first, an intuitionistic fuzzy neural network (IFNN) is developed as an estimator for time-varying PPD in both compact-form dynamic linearization (CFDL) and partial-form dynamic linearization (PFDL) for the MFAC technique. Second, two periodic event-triggered output methods are integrated with the proposed IFNN-based MFAC in both forms to save communication resources and reduce the computation burden and energy consumption. Based on the Lyapunov theory and BIBO stability approach, necessary conditions are established to guarantee the convergence of the adaptive law of the IFNN controller and the boundary of the tracking error of the closed loop system. Third, regarding the feasibility and the effectiveness of the developed control method, two simulation examples including the continuous stirred-tank reactor (CSTR) system and the heat exchanger system are given. Finally, the practical validation of the proposed data-driven control method is conducted via the speed control of a DC motor.

Intuitionistic fuzzy neural network with filtering functions. An index matrix interpretation

Biological neurons and their connection in neural networks have motivated the creation of the architecture of artificial neural networks. In the previously considered cases, the description of the neural networks and their connections are described with standard matrices where the values for the weighting coefficients and biases are placed. By recalculating them, the artificial neural network is trained. The paper presents an approach for describing multilayer neural networks with Intuitionistic Fuzzy Index Matrix (IFIM). The neural network input was described in IFIM form, then the weight coefficients of the connections between the nodes of the input vector, and then activation functions of the neurons. The use of IFIM extends the understanding and description as well as the structure and use of multilayer neural networks.

Intuitionistic Fuzzy Deep Neural Network

The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigation presents in a methodological way the whole process of IFDNN development, starting with the simplest form—an intuitionistic fuzzy neural network (IFNN) with one layer with single-input neuron, passing through IFNN with one layer with one multi-input neuron, further subsequent complication—an IFNN with one layer with many multi-input neurons, and finally—the true IFDNN with many layers with many multi-input neurons. The formulas for strongly optimistic, optimistic, average, pessimistic and strongly pessimistic formulas for NN parameters estimation, represented in the form of intuitionistic fuzzy pairs, are given here for the first time for each one of the presented IFNNs. To demonstrate its workability, an example of an IFDNN application to biomedical data is here presented.

The application of gradient evolution algorithm to an intuitionistic fuzzy neural network for forecasting medical cost of acute hepatitis treatment in Taiwan

Forecasting is important in the decision-making process. Therefore, among many forecasting algorithms that have been developed, fuzzy neural network (FNN) has been widely applied. The FNN structure is commonly trained using a back-propagation algorithm. However, this algorithm is sensitive to the initial weights and high computation, especially for complex problems. Thus, this study intended to overcome these drawbacks by applying the gradient evolution (GE) algorithm to train the intuitionistic FNN (IFNN). The proposed algorithm, GEIFNN, was verified using ten benchmark datasets. The results were compared with some other metaheuristic-based IFNN algorithms, such as genetic algorithm, particle swarm optimization algorithm, and differential evolution algorithm. The computational results show that the proposed GEIFNN relatively outperformed other tested algorithms, especially in training error. Furthermore, the proposed algorithm was applied to forecast medical costs for the treatment of acute hepatitis in Taiwan. The result also shows that GEIFNN can obtain the smallest training error.

An intelligent quantitative trading system based on intuitionistic-GRU fuzzy neural networks

Quantitative trading based on intelligent algorithms has been a hot topic in the financial fields. However, the high noises and outliers increase the uncertainty and non-stationary of financial data such that intelligent algorithms often cease to be effective in the application scenario. Therefore, how to reduce the influence of uncertainty and extract the main trends of financial data is meaningful for trading. Motivated by it, in this article, a novel trading system based on intuitionistic fuzzy neural networks with gated recurrent unit (GRU) is proposed. Firstly, empirical mode decomposition (EMD) is applied to preprocess the original data and obtain the main trends of financial time series on the basis of smoothing data. Secondly, in order to tackle with uncertainty of financial data, a novel interval type-2 intuitionistic fuzzy system (IT2IFS) is proposed for the reasoning process, where hesitancydegree is involved for the learning processes In view of the strong dependence on the time dimension of series data, the gated recurrent unit is integrated into the reasoning model to strengthen the temporal connection. Thirdly, by using the above IT2IFS-GRU model, the quantitative trading system with a concise trading strategy is constructed. By using commodity futures and foreign exchange data, the superior trading results including net profits and precisions, etc. can be obtained, which verifies the effectiveness and availability of the proposed model.

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

SummaryThis article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low‐dose X‐ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low‐dose X‐ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low‐level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and visual quality in mammograms compared with other state‐of‐the‐art methods.

Application of genetic algorithm-based intuitionistic fuzzy neural network to medical cost forecasting for acute hepatitis patients in emergency room

Application of genetic algorithm-based intuitionistic fuzzy neural network to medical cost forecasting for acute hepatitis patients in emergency room

Uncertainty handling using Improvised Intuitionistic fuzzy ANN based Voice Disorder Detection

The voice pathology detection is one of the essential process which has to be determined in the earlier stages because it is a sign for raising health related problems. The aim of this paper is to handle the uncertainty in voice dataset due to inconsistency in extracting potential features and vagueness in dealing voice signals. The raw voice signals are preprocessed by feature extraction using meyer wavelet and potential features involved in voice disorder detection are done using sequential forward feature selection methods as voice preprocessing. This research work introduced an improvised intuitionistic fuzzy artificial neural network which enhances the process of voice disorder detection is SVD database by using analytical hierarchical processing for assigning weights and thus the complete neural network performance was fine tuned instead of assigning the weights randomly. The simulation results proved the performance of the proposed model as best by producing more promising result while comparing with ANN, PANN and Fuzzy ANN models.

An intuitionistic fuzzy neural network with gaussian membership function

An intuitionistic fuzzy neural network with gaussian membership function

An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction

The prediction of time series has both the theoretical value and practical significance in reality. However, since the high nonlinear and noises in the time series, it is still an open problem to tackle with the uncertainties and fuzziness in the forecasting process. In this article, an evolving recurrent interval type-2 intuitionistic fuzzy neural network (eRIT2IFNN) is proposed for time series prediction and regression problems. The eRIT2IFNN employs interval type-2 intuitionistic fuzzy sets to enhance the modeling of uncertainties by intuitionistic evaluation and noise tolerance of the system. In the eRIT2IFNN, the antecedent part of each fuzzy rule is defined using intuitionistic interval type-2 fuzzy sets, and the consequent realizes the Takagi–Sugeno–Kang type fuzzy inference mechanism. In order to utilize the prior knowledge including intuitionistic information, a local internal feedback is established by feeding the rule firing strength of each rule to itself eRIT2IFNN is fully adaptive to the evolving of sequence data by online learning of structure and parameters. A modified density-based clustering is implemented for the structure learning, where both densities and membership degrees are involved to determine the fuzzy rules. Performance of eRIT2IFNN is evaluated using a set of benchmark problems and compared with existing fuzzy inference systems. Moreover, the eRIT2IFNN is tested for identification of dynamics under both noise-free and noisy environments. Finally, a group of practical financial price-tracking problems including high-frequency data of financial future, commodity future and precious metal are used for the evaluation of the proposed inference system.

Agriculture Crop Suitability Prediction Using Rough Set on Intuitionistic Fuzzy Approximation Space and Neural Network

Agriculture plays a vital role in Indian economy. On considering the overall geographical space verses population in India, 7% of population is chronicled in Tamilnadu, with 3% of water and 4% of land resources. Thus an automated prediction system becomes essential for predicting the crop based on the nutritional security of the country. In this paper, effort has been made to process the uncertainties by hybridizing rough set on intuitionistic fuzzy approximation space (RSIFAS) [Acharjya DP, Tripathy BK. Rough sets on intuitionistic fuzzy approximation spaces and knowledge representation. Int J Artif Int Comput Res. 2009;1 (1):29–36.] and neural network [Hecht NR. Theory of the backpropagation neural network. Proceedings of the international Joint Conference on neural networks, 1 (1989), 593–605.]. RSIFAS identifies the almost indiscernibility among the natural resources, and helps in reducing the computational procedure on employing data reduction techniques whereas neural network helps in prediction process. It helps to find the crops that may be cultivated based on the available natural resources. The proposed model is analyzed on data accumulated from Vellore district of Tamilnadu, India and achieved 93.7% of average classification accuracy. The model is compared with earlier models and found 6.9% better accuracy while prediction.

Online Evolving Interval Type-2 Intuitionistic Fuzzy LSTM-Neural Networks for Regression Problems

Since the existence of fuzziness in the real world, uncertainty could be always present in the reasoning results. Therefore, how to tackle with the hesitation existing in the process of reasoning is a meaningful issue for the construction of the inference system. In this paper, a novel interval type-2 intuitionistic fuzzy neural network based on long-short term mechanism is proposed (LSTM-IT2IFNN). By means of interval type-2 intuitionistic set, the hesitation of reasoning is described and involved to determine fuzzy rules in inference system, by which the chosen of fuzzy rules not only considers the membership value but also non-membership degree. In order to handle the regression problems with long-term time dependency, long-short term mechanism is introduced into fuzzy neural networks, which can effectively improve the memory ability of long-term knowledge. Furthermore, by means of intuitionistic sets, an extended metacognitive learning mechanism is proposed for the adaptively online learning of network structure, where it achieves when, what, and how to learn from the sequential data. The performance of the LSTM-IT2IFNN is evaluated on different regression problems, including synthetic and real-world data. By comparing to existing algorithms, the experimental results show the efficiency and accuracy of the proposed scheme.

A Fuzzy Method for Assessing Eco-Environmental Disaster Risk Caused by Coalbed Methane in China

Based on an improved risk assessment index system, this paper constructs an eco-environmental disaster risk assessment model during coalbed methane industrialization development in China by using intuitionistic fuzzy sets to describe the uncertain risk information, and the transformed interval value of Mamdani intuitionistic fuzzy neural networks model. Then, the validity of the model is verified by simulation tests. Furthermore, the assessment results are compared with those obtained by fuzzy neural networks model. Results show that suggested model has multidimensional nonlinearity and global approximation characteristics. By the procedure of “fuzzification -fuzzy-rules - defuzzifier”, the output conversion from uncertainty quantitative indicators to accurate risk assessment values can be effectively realized. Compared with the fuzzy neural networks model, the suggested model has better accuracy and stability. The risk assessment value calculated by the suggested model fairly matches the expected one. The study supplies a decision support for routine supervision and risk precaution and management on one hand, and enriches the theoretical research of the eco-environmental risk assessment of coalbed methane industrialization development on the other hand.

Intrusion detection techniques based on improved intuitionistic fuzzy neural networks

At present, the issue of intrusion detection has been a hot point in the computer security area. In this paper, three novel intrusion detection schemes have been proposed. First, unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFSs) and artificial neural networks (ANNs) together, which lead to much fewer iteration numbers, higher detection rates and sufficient stability. Then, we address anther novel scheme based on non-subsampled shearlet transform (NSST) domain ANNs to solve those problems, including employing multi-scale geometry analysis (MGA) of NSST and the train characteristics of ANN together. Lastly, an efficient anomaly analysis proves to be more efficient and less complex than the existing techniques. The approach relies on monitoring the security state by using a set of accurate metrics. NSST is used to decompose these metrics. Attacks are viewed as singularities that arise in some specific points of time.

Intrusion detection techniques based on improved intuitionistic fuzzy neural networks

At present, the issue of intrusion detection has been a hot point in the computer security area. In this paper, three novel intrusion detection schemes have been proposed. First, unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFSs) and artificial neural networks (ANNs) together, which lead to much fewer iteration numbers, higher detection rates and sufficient stability. Then, we address anther novel scheme based on non-subsampled shearlet transform (NSST) domain ANNs to solve those problems, including employing multi-scale geometry analysis (MGA) of NSST and the train characteristics of ANN together. Lastly, an efficient anomaly analysis proves to be more efficient and less complex than the existing techniques. The approach relies on monitoring the security state by using a set of accurate metrics. NSST is used to decompose these metrics. Attacks are viewed as singularities that arise in some specific points of time.

Application and Analysis of Fuzzy Neural Network Algorithm Based on Cloud Computing Platform in Enterprise Network Marketing

The emergence of cloud computing provides a convenient and efficient tool for solving the problem of enterprise network marketing. Cloud computing service mode can effectively reduce the cost of software and hardware, and flexibility to adapt to the needs of enterprises in different stages of development. Therefore, the construction mode of cloud computing will effectively solve the problem of enterprise network marketing. Combined with the characteristics of enterprise system and management, cloud computing can achieve data and application sharing between different devices with lower equipment requirements. With the characteristics of network marketing, this paper analyzes the basic structure of cloud computing, and proposes a kind of intuitionistic fuzzy neural network algorithm which is based on cloud computing. First, we divide the training sample into several sub blocks, and then put each sub block into the training network. After the training, we put the results into the membership network and non membership network. At the same time, we take the test sample to complete the calculation of the membership network and non membership network. Finally, the output value of the test sample and the training sample is synthesized by intuition, and the output value of the intuitionistic fuzzy neural network is obtained. In addition, we use MAP-REDUCE to determine the connection weights of neural networks in cloud computing. This effectively solves the problem of big data and long time consuming in the data mining network marketing information. Finally, we validate the algorithm on the cloud computing platform. The experimental results show that this paper has good application effect and prospect in the field of enterprise network marketing.

Intrusion Detection Technique Based on Improved Intuitionistic Fuzzy Neural Networks

At present, the issue of intrusion detection has been a hot point to all over the computer security area. In this paper, a novel intrusion detection method has been proposed. Unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFS) and artificial neural networks (ANN) together, which leads to much fewer iteration numbers, higher detection rates and sufficient stability. Experimental results show that the now method proposed in this paper is promising and has obvious superiorities over other current typical ones.

Novel applications of intuitionistic fuzzy digraphs in decision support systems.

Many problems of practical interest can be modeled and solved by using graph algorithms. In general, graph theory has a wide range of applications in diverse fields. In this paper, the intuitionistic fuzzy organizational and neural network models, intuitionistic fuzzy neurons in medical diagnosis, intuitionistic fuzzy digraphs in vulnerability assessment of gas pipeline networks, and intuitionistic fuzzy digraphs in travel time are presented as examples of intuitionistic fuzzy digraphs in decision support system. We have also designed and implemented the algorithms for these decision support systems.

Data mining method based on adaptive intuitionistic fuzzy inference

A new method was proposed for data mining problem which integrated intuitionistic fuzzy set and neural network theory.This article solved the data mining problem with the method of adaptive intuitionistic fuzzy inference.This method can adjust network variables by use of intuitionistic neural network's adaptive learning function,and generate rules bank automatically.Finally,this method was validated by simulation.

Technique for detecting group events based on intuitionistic fuzzy ART neural network

The target classification, the group proceeding and the detection for group events were described. Being associated with the concept of Closeness in intuitionistic fuzzy sets, the model of intuitionistic fuzzy Adaptive Resonance Theory (ART) neural network was built. How to run the network and how to adjust the weights of the network were designed. Effect of targets classification using intuitionistic fuzzy ART neural network was shown by an example.