A model for classifying information objects using neural networks and fuzzy logic

Provides an abstract of the presentation and a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.

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.

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>

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.

Over the last few decades, fuzzy logic has been shown as a powerful methodology for dealing with imprecision and nonlinearity efficiently. Applications can be found in a wide context ranging from medicine to finance, from human factors to consumer products, from vehicle control to computational linguistics, and so on (Wang 1997; Dubois and Prade 2000; Passino and Yurkovich 1998; Jang et al. 1997; Sugeno 1985; Pedrycz 1993). However, one of the shortcomings of fuzzy logic is the lack of systematic design. To circumvent this problem, fuzzy logic is usually combined with Neural Networks (NNs) by virtue of the learning capability of NNs. NNs are networks of highly interconnected neural computing elements that have the ability of responding to input stimuli and learning to adapt to the environment. Both fuzzy systems and NNs are dynamic and parallel processing systems that estimate input-output functions (Mitra and Hayashi 2000). The merits of both fuzzy and neural systems can be integrated in Fuzzy Neural Networks (FNNs) (Lee and Lee 1974, 1975; Pal and Mitra 1999; Zanchettin and Ludermir 2003). Therefore, the integration of fuzzy and neural systems leads to a symbiotic relationship in which fuzzy systems provide a powerful framework for expert knowledge representation, while NNs provide learning capabilities.

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.

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.

This paper demonstrates that fuzzy logic systems and feedforward neural networks are equivalent in essence. First, we introduce the concept of interpolation representations of fuzzy logic systems and several important conclusions. We then define mathematical model for rectangular wave neural networks and nonlinear neural networks. With this definition, we prove that nonlinear neural networks can be represented by rectangular wave neural networks. Based on this result, we prove the equivalence between fuzzy logic systems and feedforward neural networks. This result provides us a very useful guideline when we perform theoretical research and applications on fuzzy logic systems, neural networks, or neuro-fuzzy systems.

Introduction: This paper presents a novel approach for classifying different types of steel rods using a combination of pre-trained convolutional neural networks (CNNs) and fuzzy logic systems. We utilize VGG16, ResNet50, and InceptionV3 models to extract features from grayscale images of steel rods, which are then classified using Support Vector Machines (SVM). Furthermore, we incorporate a fuzzy logic system to improve the final classification accuracy by combining the individual accuracies of each CNN model. Experimental results demonstrate the effectiveness of our approach in achieving high classification accuracy compared to traditional methods. Objectives: This research presents a novel multi-model approach for steel rod classification utilizing three distinct fuzzy logic systems: Fuzzy Recurrent Neural Network (RNN), Fuzzy Convolutional Neural Network (CNN), and Fuzzy Neural Network. The proposed system addresses the critical challenge of accurate defect detection and classification in steel manufacturing processes. By combining these complementary approaches, we achieve enhanced feature extraction capabilities and improved classification accuracy. Experimental results demonstrate that our multi-model system achieves a classification accuracy of 97.8%, surpassing single-model approaches by an average of 8.2%. The system successfully identifies surface defects, structural anomalies, and dimensional variations with high precision, making it suitable for real-time industrial applications. Methods: This paper introduces a novel approach for the feature extraction and classification of steel rods by integrating multiple models and utilizing fuzzy logic systems. The methodology leverages pre-trained convolutional neural networks (CNNs) for extracting comprehensive feature representations from steel rod images. These extracted features are then combined and integrated using fuzzy logic to enhance classification accuracy and reliability. By addressing the limitations of traditional manual inspection methods and single-model approaches, this integrated multi-model system offers a robust solution for real-time industrial implementation. The proposed system aims to improve the efficiency and effectiveness of quality control processes in steel rod manufacturing, ensuring consistent and accurate classification even under varying conditions and complexities. This innovative approach paves the way for more advanced and adaptable automated inspection systems in the industry. Results: The paper aims to develop an integrated multi-model system that combines three distinct fuzzy logic approaches to enhance feature extraction capabilities through complementary methodologies. By improving the classification accuracy and reliability, the system addresses the limitations of existing methods. The goal is to create a robust and adaptable system that is suitable for real-time industrial implementation for steel rod analysis and classification. This comprehensive approach leverages the strengths of multiple models and fuzzy logic techniques, ensuring accurate and efficient classification processes, even under complex and varying conditions. The proposed multi model system is designed to meet the stringent quality control requirements of industrial applications, ultimately improving productivity and product quality through reliable automated inspection and classification for steel rod with accuracy of 97.80%, precision of 97.30% and recall 97.50%. Conclusions: This paper presents a novel approach for the classification of steel rods using a combination of pre-trained CNN models and fuzzy logic systems. The experimental results demonstrate the effectiveness of our method in achieving high classification accuracy. This research demonstrates the effectiveness of a multi-model fuzzy logic approach for steel rod classification. The system achieves significant improvements over single-model approaches while maintaining real-time processing capabilities. Our approach provides a foundation for further research and development in automated industrial image classification.

Fuzzy logic provides a method for representing analog processes in a digital framework. Processes that are implemented through fuzzy logic are often not easily separated into discrete segments and may be difficult to model with conventional mathematical or rule-based paradigms that require hard boundaries or decisions, i.e., binary logic where elements are either a member of a given set or they are not. Consequently, fuzzy logic is valuable where the boundaries between sets of values are not sharply defined or there is partial occurrence of an event. In fuzzy set theory, an element's membership in a set is a matter of degree. This chapter describes the concepts inherent in fuzzy set theory and applies them to the solution of the inverted-pendulum problem and a Kalman-filter problem. Fuzzy and artificial neural network concepts may be combined to form adaptive fuzzy neural systems where either the weights and/or the input signals are fuzzy sets. Fuzzy set theory may be extended to fuse information from multiple sensors as discussed in the concluding section.

A pattern recognition approach to data fusion in Intelligent Vehicle Highway Systems : Palacharla, Prasad V., Ph.D. University of Illinois at Chicago, 1995. 78 pp.

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.

Implementation of fuzzy logic systems and neural networks in industry

In the evolving realm of autonomous vehicle navigation, the integration of fuzzy logic and neural networks presents a formidable challenge, particularly in the context of real-time, on-the-fly neural network training. This paper addresses the gap in dynamic and adaptable training methods necessary for navigating unpredictable environments with limited computational resources. The primary objective of our study is to empirically validate a hybrid training approach that combines fuzzy logic with back-propagation learning algorithms, aiming to optimize neural network performance under hardware constraints. Our methodology leverages a fuzzy logic trainer to provide initial training sets dynamically, which guide the neural network in adjusting its weights in real time, thus facilitating adaptive learning during navigation tasks. The findings reveal that this integrated approach not only enhances the learning efficiency of neural networks but also significantly improves navigation accuracy in real-time scenarios. These advancements contribute to the field by demonstrating the feasibility of deploying more adaptable and robust autonomous navigation systems, potentially expanding their application in more diverse and challenging environments.

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.