2,979 publications from this institution
One of the problems of existing fuzzy-neural approaches is that the logic nature of the structure is often lost, i.e., what is being processed by the neural networks becomes irrelevant. To retain this logic content while benefiting from the advantage of integrating fuzzy set and neural network approaches, we propose in this paper a fuzzy neural network which supports fuzzy inference mechanisms by being based exclusively on logic implication neurons. A supervised learning method involving an equality performance measure and an online update delta rule (gradient-based) learning procedure is used. An experimental study involving Wolfer's sunspot numbers is carried out, demonstrating fast convergence accompanied by explicit format of the inference network.
This paper deals with a formal description of ill-defined processes (fuzzy systems) by the use of fuzzy relational equations. It is pointed out that fuzzy relational equations form a generalized version of the difference equations widely considered in control theory. Some equivalence between these two kinds of description is presented. Basic problems of fuzzy systems e.g. identification, prediction, sensitivity and stability are shown and numerical algorithms are given. Indices of each method are introduced (especially the degree of fuzziness, the sensitivity index) which makes it possible to express the quality of each of them.
This study is concerned with a general methodology of identification of fuzzy models. Unlike numeric models, fuzzy models operate at a level of information granules - fuzzy sets - and this aspect brings up an important design requirement of transparency of the model. We propose a three-phase development framework by distinguishing between structural and parametric optimization processes. The underlying topology of the model dwells on fuzzy neural networks - architectures governed by fuzzy logic and equipped with parametric flexibility. Two general optimization mechanisms are explored: the structural optimization is realized via genetic programming whereas for the ensuing detailed parametric optimization we proceed with gradient-based learning. The main advantages of this approach are discussed in detail. The study is illustrated with the aid of a numeric example that provides a detailed insight into the performance of the fuzzy models and quantifies crucial design issues.
In order to optimize fuzzy modeling of nonlinear system, we proposed a optimal fuzzy model according to the characteristic of I/O relationship, hard c-mean method, genetic algorithm, and objective function with weighting factor. A conventional fuzzy model has difficulty in definition of membership function. In order to solve its problem, the premise structure of the proposed fuzzy model is selected by both the partition of input space and the analysis of input-output relationship using the clustering algorithm. The premise parameters of the fuzzy model are optimized respectively by the genetic algorithm and the consequence parameters of the fuzzy model are identified by the standard least square method. Also, an aggregate objective function with weighting factor is proposed to achieve a balance between the performance results for the training and testing data.
<title>Abstract</title> Image compression techniques realized in various ways have become an indispensable part in the practical storage and transmission of digital images. In this study, we present a novel method of lossy compression based on sampling and fuzzy encoding for grayscale images and discuss the problem of their reconstruction. First, an image is divided into a number of non-overlapping blocks of pixels. Next, we perform multiple rounds of random sampling. In each round, a number of pixels are selected as prototypes for the representing the corresponding block. Each pixel in the block is reconstructed based on the gray-levels of the prototypes and membership degrees computed with respect to the distances of each pixel to the prototypes. The reconstruction abilities delivered by the prototypes are quantified by a certain objective fidelity criterion and the prototypes leading to lowest reconstruction error are determined as representatives of current block. Finally, once the representatives in each block have been determined, we reconstruct the whole image based on these prototypes. Experimental studies as well as visual evaluations show that the proposed algorithm is able to achieve high compression ratios while preserving the overall fidelity in the decompressed images.
The Bandler-Kohout subproduct (BKS) method acts as one of the two representative fuzzy relational inference (FRI) strategies. Observing the BKS method using constraint modeling, two fuzzy implications, respectively, produce expression to the factors of inference mechanism and rule base. However, these two factors normally reflect different connotations from the perspectives of artificial intelligence applications and logical meaning. Enlightened by such idea, in this study, we propose and investigate the differently implicational BKS (DBKS) method. Initially, main properties of DBKS are validated. The reversibility and interpolativity of DBKS are proved under certain conditions. The equivalent relationship is verified between interpolativity and continuity for DBKS. The robustness of DBKS is confirmed from both the similarity and the extensional hull. Posteriorly, the computational performance of DBKS is analyzed. In DBKS, the preservation of the indistinguishability holds for input fuzzy sets, and it is proved that the first-aggregate-then-infer (FATI) reasoning strategy of DBKS is equivalent to the first-infer-then-aggregate (FITA) one. To improve the computational efficiency, the hierarchical DBKS method is presented. In addition, the fuzzy system is established on the strength of the DBKS method, the singleton fuzzifier and the centroid defuzzifier. Its response function is analyzed and a universal approximator is built by the fuzzy system via DBKS. At the end, we compare the results of DBKS with BKS by virtue of two examples in affective computing. It is discovered that DBKS can create superior forms of FRI in comparison to those produced by BKS.
Fuzzy measures and Choquet integral are efficient aggregation operators utilised intensively in decision-making theory. To produce sound classification results based on a family of classifiers, the parameters of the fuzzy measure (especially, so-called fuzzy densities) have to be determined. In this study, we propose a method based on particle swarm optimisation (PSO) and discuss in detail a new concept of a so-called positive and negative optimisation to fully utilise specific properties of classifiers to carry out efficient classification. A suite of experiments is conducted to illustrate this approach and discuss its scope of applicability.
