International Journal on Artificial Intelligence Tools

In this approach, we investigate the fuzzy γ-models for decision analysis and making. This methodology utilizes fuzzy γ-model as an information aggregation operator. It provides several advantages due to the fact that the input to each model is the evidence supplied by the degree of satisfaction of sub-criteria and the output is the aggregated evidence. We also generalize fuzzy γ-models as a hierarchical network in this work. Thus, the decision making process is to aggregate and propagate the evidence information through such a hierarchical network. This trainable network is able to perceive and interpret complex decisions by using those fuzzy models. The simulation study examines the learning behaviors of the fuzzy γ-models using two numerical examples.

A novel extension-based fuzzy model is proposed in this paper. The newly established extension theory is integrated into the conventional fuzzy system to enhance the reasoning capability. In parameter identification process, adjusting a membership function to satisfy one pattern may deteriorate the others performance and result in a lengthy turning process. This incompatible issue is alleviated by the extension theory. We will investigate how to define the extended relational functions and how to refine the roughly designed model to meet the system requirement. During the refining process, both the fired and the neighborhood of the fired membership functions are adjusted simultaneously. Simulation results from single-input-single-output and double-input-single-output models verified that better results than the conventional methods have been obtained.

The purpose of this paper is to apply adaptive fuzzy sliding mode control for the structural control of base-isolated buildings. Combining the fuzzy control and sliding mode control one can reduce the complexity of fuzzy rule bases. It also ensures the stability and robustness. The Lyapunov theory is used to develop the adaptive law. In addition to full-state-feedback controller, an estimator using only the measured information from a few sensors is presented. Finally, the adaptive sliding mode control is applied to two types of base-isolated building, with LRB, and sliding bearing isolators. Stiffness uncertainty and time delay is utilized to illustrate the robustness of this proposed algorithm. The effectiveness of this algorithm is demonstrated by simulation results for a long period, a wide-pass white noise artificial earthquake and Taiwan Chi Chi earthquake occurred in 1999. The simulations show that adaptive sliding mode control can achieve satisfactory results in the application of structural control for base-isolated buildings.

The proliferation of information on the World Wide Web has made the personalization of this information space a necessity. An important component of Web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. In the absence of any a priori knowledge, unsupervised classification or clustering methods seem to be ideally suited to analyze the semi-structured log data of user accesses. In this paper, we define the notion of a "user session" as being a temporally compact sequence of Web accesses by a user. We also define a new distance measure between two Web sessions that captures the organization of a Web site. The Competitive Agglomeration clustering algorithm which can automatically cluster data into the optimal number of components is extended so that it can work on relational data. The resulting Competitive Agglomeration for Relational Data (CARD) algorithm can deal with complex, non-Euclidean, distance/similarity measures. This algorithm was used to analyze Web server access logs successfully and obtain typical session profiles of users.

This article proposes a novel algorithm for optimizing a nonlinear dynamic system subjected to flexible path constraints. Each flexible constraint is fuzzified by using the concept of degree-of-acceptability, and the fuzzy degree-of-satisfaction for the objective is then derived. The numerical method of Integrated Controlled Random Search for Dynamic System (ICRS/DS) is applied here for solving the resulting fuzzy decision problem. One numerical example is supplied, demonstrating the applicability of the proposed algorithm.

This paper proposes a new reasoning technique on fuzzy production systems while given input knowledge is incomplete. Based on the fuzzy Petri net formalism, the proposed algorithm can infer all possible conclusions and their corresponding missing inputs. The most possible conclusion can also be determined based on the criteria of the minimum number of missing inputs as well as the degree of truth of the conclusion. In addition, finiteness and computational complexity of the algorithm is investigated. As real decisions are typically made under incomplete input knowledge, this reasoning technique provides more realistic applications for fuzzy production systems.

This paper proposes a method to construct a subjective model expressing color impressions, which is expressed by fuzzy rules. Hue and tone are dealt with in the premise part of fuzzy rules and the degree of color impressions is expressed in the consequent part. Color impressions are the basic concept known as psychological effects of colors. In this paper two pieces of subjective information are considered in order to construct the subjective model. One is the information of the absolute relationship between a color and the subjective degree of its impressions. And the other is the one about the relative relationship between colors on a subjective measure in terms of color impressions. Two kinds of questionnaires, called the absolute and the comparative judgments, are performed to get the subjective information. Additionally, this paper proposes improves the method so that is more applicable to the practical use. A validity of the proposed methods are examined by evaluation experiments.

The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and numerical information based on high-level fuzzy Petri nets (HLFPN). Fuzzy functions, fuzzy reasoning, and fuzzy neural networks are integrated in HLFPN In HLFPN model, a fuzzy place carries information to describe the fuzzy variable and the fuzzy set of a fuzzy condition. An arc is labeled with a fuzzy weight to represent the strength of connection between places and transitions. A fuzzy set and a fuzzy truth-value are attached to an uncertain fuzzy token to model imprecision and uncertainty. We have identified six types of uncertain transition: calculation transitions to compute functions with uncertain fuzzy inputs; inference transitions to perform fuzzy reasoning; neuron transitions to execute computations in neural networks; duplication transitions to duplicate an uncertain fuzzy token to several tokens carrying the same fuzzy sets and fuzzy truth values; aggregation transitions to combine several uncertain fuzzy tokens with the same fuzzy variable; and aggregation-duplication transitions to amalgamate aggregation transitions and duplication transitions. To guide the computation inside the HLFPN, an algorithm is developed and an example is used to illustrate the proposed approach.