A self-pruning binary tree (SPBT) interconnection network architecture that tolerate faults in a wafer scale integration (WSI) environment is proposed. The goal of the SPBT network is to provide a reliable and a quickly reconfigured interconnection network architecture for linear WSI arrays. The proposed architecture uses a bottom-up approach to reconfigure a linear pipelined array on a potentially defective WSI array using a binary tree interconnection scheme. The binary tree is generated by successive formation of hierarchical modules. For N processing elements (PEs) on the wafer, reconfiguration time is O(log N). The propagation delay is bounded by Theta (log N) and is independent of the number of faulty PEs. Faults in the switching network as well as faulty processing elements are tolerated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
In this paper, we introduce a new topology of fuzzy polynomial neural networks (FPNNs) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs). The study offers a comprehensive design methodology involving mechanisms of genetic optimization, especially those exploiting genetic algorithms (GAs). Let us recall that the design of the "conventional" FPNNs uses an extended group method of data handling (GMDH) and uses a fixed scheme of fuzzy inference (such as simplified, linear, and regression polynomial fuzzy inference) in each FPN of the network. It also considers a fixed number of input nodes (as being selected in advance by a network designer) at FPNs (or nodes) located in each layer. However such design process does not guarantee that the resulting FPNs will always result in an optimal networks architecture. Here, the development of the FPNN gives rise to a structurally optimized topology and comes with a substantial level of flexibility which becomes apparent when contrasted with the one we encounter in the conventional FPNNs. The design of each layer of the FPNN deals with its structural optimization involving a selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial forming a consequent part of fuzzy rules and a collection of the specific subset of input variables) and addresses detailed aspects of parametric optimization. Along this line, two general optimization mechanisms are explored. The structural optimization is realized via GAs. In case of the parametric optimization we proceed with a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network becomes generated in a dynamic fashion. To evaluate the performance of the genetically optimized FPNN (gFPNN), we experimented with two time series data (gas furnace and chaotic time series) as well as some synthetic data. A comparative analysis reveals that the proposed FPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.
Fuzzy neural networks combining advantages of numerical computations of neural networks and symbolic processing originating from artificial intelligence are typical constructs of computational intelligence. The paper analyzes basic processing components (fuzzy neurons), proposes several general architectures, elaborates on learning algorithms, and provides a series of application cases.
Abstract Given the pressing need for explainability in Machine Learning systems, the studies on counterfactual explanations have gained significant interest. This research delves into this timely problem cast in a unique context of relational systems described by fuzzy relational equations. We develop a comprehensive solution to the counterfactual problems encountered in this setting, which is a novel contribution to the field. An underlying optimization problem is formulated, and its gradient-based solution is constructed. We demonstrate that the non-uniqueness of the derived solution is conveniently formalized and quantified by admitting a result coming in the form of information granules of a higher type, namely type-2 or interval-valued fuzzy set. The construction of the solution in this format is realized by invoking the principle of justifiable granularity, another innovative aspect of our research. We also discuss ways of designing fuzzy relations and elaborate on methods of carrying out counterfactual explanations in rule-based models. Illustrative examples are included to present the performance of the method and interpret the obtained results.
In this paper, we introduce a new topology of fuzzy-neural networks-fuzzy-set-based polynomial neural networks (FSPNNs). The two underlying design mechanisms of such networks involve genetic optimization and information granulation (IG). The resulting constructs come in the form of fuzzy polynomial neural networks with fuzzy-set-based polynomial neurons, regarded as their generic processing elements. First, we introduce a comprehensive design methodology using which we determine the optimal structure of the FSPNNs. This methodology hinges on the extended group method of data handling and fuzzy-set-based rules. It concerns the optimization of the FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit a notion of information granules defined over a system's variables and formed through the process of IG. This granulation is realized with the aid of the hard C-means clustering algorithm. The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.
