An adaptive teleoperate mobile robot controller based on fuzzy neural network

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This paper presents an improved fuzzy-neural network (FNN) model, which is a simple but effective fuzzy-rule based model about complex systems. Its architecture could be determined only by using the input-output data of the system. In the FNN model, the membership functions and weights would be adjusted optimally through the high speed BP learning principle, and the number of neurons in the inference layer would be confirmed through fuzzy data curves. Viewing the neural network as a fuzzy model gives an insight into the real system, and provides a method to simplify the neural network.

This chapter provides a comprehensive review of fuzzy neural network models. Existing methods to combine fuzzy logic and neural networks are presented. Several classifications of fuzzy neural networks are provided. Those classifications are based on the type of application of the fuzzy neural network. Fuzzy neural network models with supervised learning features are also reviewed in detail. A review of various biomedical engineering applications that use fuzzy neural networks is also presented. Some future medical science applications of fuzzy neural networks are described. A complete bibliography is listed at the end of the chapter.

Classification of seven kinds of dyeing defects is proposed using image processing and fuzzy neural network approaches. The defects include filling band in shade, dye and carrier spots, mist, oil stain, tailing, listing, and uneven dyeing on selvage. The fuzzy neural classification system is constructed by a fuzzy expert system with the neural network as a fuzzy inference engine. The neural network is trained to become the inference engine using sample data. This fuzzy neural network system possesses merits of both fuzzy logic and neural networks, and thus is more intelligent in handling pattern recognition and classification problems. Region growing is adopted to directly detect different defect regions in an image. In all, seventy samples, ten samples for each defect, are obtained for training and testing. The results demonstrate that the fuzzy neural network approach can precisely classify these samples by the features selected.

Ambiguity is always present in any realistic process. This ambiguity may arise from the interpretation of the data inputs and in the rules used to describe the relationships between the informative attributes. Fuzzy logic provides an inference structure that enables the human reasoning capabilities to be applied to artificial knowledge-based systems. For efficient working the artificial knowledge-based systems depend upon algorithms which are cumbersome to implement and require extensive computational time. On the other hand, the human brain which performs approximate reasoning employs simple information processing elements called neurons. The paradigm of artificial neural networks, developed to emulate some of the capabilities of the human brain, has demonstrated a great potential in terms of learning and adaptation for various applications such as system identification and control, pattern recognition, prediction, etc. They provide low-level computations and embodies salient features such as learning, fault-tolerance, parallelism and generalization. On the other hand, fuzzy logic provides a means for converting linguistic strategy into control actions and thus offering a high-level computation. Although, fuzzy logic and artificial neural networks are both functionally and structurally different, it is envisaged that the synthesis of these two areas will give rise to a new paradigm called fuzzy neural networks. The latter have the potential to capture the benefits of both the fields, fuzzy logic and neural networks, into a single paradigm. The objective of this paper is to describe the basic concepts of fuzzy neural networks. Towards this goal, a fuzzy neural structure based on the notion of T-norm and T-conorm is developed. A fuzzy cellular neural network as applied to image enhancement is also described in this paper.

The fuzzy logic and neural networks are combined in this paper, setting up the fuzzy neural network (FNN); meanwhile, the distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear power plant, and the intelligence fault diagnostic system of the nuclear power plant is built based on the FNN. The fault symptoms and the possibility of the inverted U-tube break accident of steam generator are discussed. In order to test the system’s validity, the inverted U-tube break accident of steam generator is used as an example and many simulation experiments are performed. The test result shows that the FNN can identify the fault.

PurposeThis paper aims to optimize supply chain information decision-making systems to better manage complex, high-dimensional and uncertain information through the integration of fuzzy logic and neural network technology.Design/methodology/approachA framework based on fuzzy logic reasoning is developed to address empirical issues in traditional supply chain systems. Subsequently, an innovative radial basis function-dynamic fuzzy neural network (RBF-DFNN) model is constructed, enhancing the system’s capability to interpret uncertain information. This model retains the advantages of traditional dynamic fuzzy neural networks (DFNN) while introducing an anti-fuzzy layer and optimizing the membership function and T-paradigm layers.FindingsThe RBF-DFNN model leads to the creation of a high-dimensional information decision-making model for supply chains. Experimental results indicate that this model effectively utilizes the K-medoids clustering algorithm to accurately capture the high-dimensional characteristics and intrinsic correlations of supply chain data. Parameter optimization significantly improves the model’s performance, with the root mean squared error (RMSE) and mean absolute error (MAE) enhanced, resulting in coefficients of determination rising from 95.6 and 97.8–99.1% compared to STPF-AIMM and ANFIS networks.Originality/valueThis study contributes to the advancement of supply chain management by developing a highly intelligent and refined decision-making model, enhancing the intelligence level of intelligent storage systems and promoting more sophisticated supply chain operations.

In order to construct a simulation model for estimating the effluent chemical oxygen demand (COD) value of an activated sludge process in a “U” plant, fuzzy neural network (FNN) was applied. The constructed FNN model could simulate periodic changes in COD with high accuracy. Comparing the simulation result obtained using the FNN model with that obtained using the multiple regression analysis (MRA) model, it was found that the FNN model had 3.7 times lower error than the MRA model. The FNN models corresponding to each of the four seasons were also constructed. Analyzing the fuzzy rules acquired from the FNN models after learning, the operational characteristic of this plant could be elucidated.

Over the last decade, an extensive research has been carried out in the areas of fuzzy logic and neural networks. Fuzzy logic has emerged as a mathematical tool to deal with the uncertainties in human perception and reasoning. It also provides a framework for an inference mechanism that allows for approximate human reasoning capabilities to be applied to knowledge-based systems. On the other hand, artificial neural networks have emerged as fast computation tools with learning and adaptivity capabilities. Recently, these two fields have been integrated into a new emerging technology called fuzzy neural networks which combines the benefits of each field. The objective of the paper is to establish the similarities and differences between fuzzy systems and neural networks and to discuss possible models for fuzzy neural networks which can be applied to system modeling and control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper investigates the warp let-off and take-up mechanism of rapier looms to solve the problem that the warp tension of rapier looms fluctuates greatly and the warp let-off is difficult to maintain constant. The design and hardware implementation of a let-off and take-up control system based on fuzzy neural network (FNN) and vector control (VC) are presented to improve the control level of warp tension and drive performance of the let-off and take-up system. Firstly, the spring-damper dynamic model of the warp is established according to the mechanical properties. The parametric expression of warp tension and the control strategy of fixed angle interval based on let-off and take-up motions are constructed according to the generation mechanism and fluctuation law of warp tension. Then, based on fuzzy reasoning mechanism and neural network model, the fusion theory of fuzzy neural network is introduced, and a tension controller based on T-S fuzzy neural network (FNN) is designed. FNN is trained by introducing genetic optimization and the backpropagation fusion algorithm (GA-BP). In addition, a specialized let-off and take-up hardware circuit is constructed through embedded technology, and the SVPWM algorithm is used as the driving scheme of the hardware circuit. Finally, simulation and actual weaving experiments test the proposed let-off and take-up control system and hardware circuit. The results show that, compared to PID and fuzzy PID, the proposed fuzzy neural network algorithm has higher tension control accuracy and can effectively restrain the rapier loom’s warp tension undulation. The designed hardware circuit and SVPWM algorithm have a fast and stable driving ability, which ensures the constant let-off amount.

The present study aims at the application of the hybrid model, which consists of artificial neural network and fuzzy logic in the reservoir operating policy during critical periods. The proposed hybrid model [fuzzy neural network (FNN)] combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The FNN model is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. The FNN model has been developed to study the behavior of optimal release operating policy on the proposed reservoir in Pagladiya River of the Assam State in India. Here, reservoir operation policies were formulated through dynamic programming. The optimal release was related to storage, inflow, and demand. The advantages of using the FNN model in reservoir release are discussed using the case study.

When combining fuzzy logic and neural networks it is possible to get a hybrid system that can process uncertain values and can be trained. Fuzzy logic elements can be regarded as fuzzy-neural networks. In order to present a set of fuzzy values the ternary encoding is used. Three fuzzy neural networks on linear neurons are proposed. The first operates as a fuzzy logical minimum element, the second does as a fuzzy logical maximum element, the third - as a fuzzy logical complement element.

Use of fuzzy neural networks in modeling relationships of HPV infection with apoptotic and proliferation markers in potentially malignant oral lesions

Image acquisition is an essential step in image processing. When the image acquisition is done the image that is generated is subjected to Impulse noise, Gaussian noise etc. We have performed the image denoising on images inflicted with impulse noise. Image denoising is an essential step in all types of image processing. Traditional techniques reduce the noise in the image but it also reduces the quality of the image. Traditional filters like gaussian filter, median filter is analyzed which work in the spatial domain and filters working in the frequency domain are also considered like Butterworth filters, Weiner filter. A Deep residual Neural Network filter is proposed which is compared with the Fuzzy Neural Network denoiser. Their performance is compared on the metrics PSNR and SSIM. The Fuzzy Neural Network system improves the SSIM significantly compared to a deep residual neural network and a comparison is made with traditional image denoising methods. We also compare the performance of the deep residual neural network, Fuzzy Neural Network system and Median denoising algorithm on impulse noise has been compared. The performance of deep neural networks depends on the total number of examples used and the performance can be improved if we have more image pairs.

This paper presents a FPGA based approach for a modular architecture of Fuzzy Neural Networks (FNN) to embed with easily different topologies set up. The project is based on a Takagi - Hayashi (T-H) method for the construction and tuning of fuzzy rules, this is commonly referred as neural network driven fuzzy reasoning. The proposed architecture approach consists of two main configurable modules: a Multilayer Perceptron - MLP with sigmoidal activation function that composes the first module to determine a Fuzzy membership function; the second employs an MLP with pure linear activation function to define the consequents. The DSPBuilder® software along the Simulink® is used to connect, set and synthesize the Fuzzy Neural Network desired. Other hardware components employed in the architecture proposed cooperate to the system modularity. The system was tested and validated through a control problem and an interpolation problem. Several papers proposed different hardware architecture to implement hybrid systems by using Fuzzy logic and Neural Network. However, there is no approach with this specific neural network driven fuzzy reasoning by T-H method and the aim to be embedded. The Self-Organizing Map (SOM) and Levenberg-Marquardt backpropagation were used to train the FNN proposed off-line.