17329512
Hybrid compensation control for affine TSK fuzzy control systems
The paper proposes a way of designing state feedback controllers for affine Takagi-Sugeno-Kang (TSK) fuzzy models. In the approach, by combining two different control design methodologies, the proposed controller is designed to compensate all rules so that the desired control performance can appear in the overall system. Our approach treats all fuzzy rules as variations of a nominal rule and such variations are individually dealt with in a Lyapunov sense. Previous approaches have proposed a similar idea but the variations are dealt with as a whole in a robust control sense. As a consequence, when fuzzy rules are distributed in a wide range, the stability conditions may not be satisfied. In addition, the control performance of the closed-loop system cannot be anticipated in those approaches. Various examples were conducted in our study to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired.
43779210
Support vector interval regression networks for interval regression analysis
In this paper, the support vector interval regression networks (SVIRNs) are proposed for the interval regression analysis. The SVIRNs consist of two radial basis function networks. One network identifies the upper side of data interval, and the other network identifies the lower side of data intervals. Because the support vector regression (SVR) approach is equivalent to solving a linear constrained quadratic programming problem, the number of hidden nodes and the initial values of adjustable parameters can be easily obtained. Since the selection of a parameter e in the SVR approach may seriously affect the modeling performance, a two-step approach is proposed to properly select the e value. After the SVR approach with the selected e, an initial structure of SVIRNs can be obtained. Besides, outliers will not significantly affect the upper and lower bound interval obtained through the proposed two-step approach. Consequently, a traditional back-propagation (BP) learning algorithm can be used to adjust the initial structure networks of SVIRNs under training data sets without or with outliers. Due to the better initial structure of SVIRNs are obtained by the SVR approach, the convergence rate of SVIRNs is faster than the conventional networks with BP learning algorithms or with robust BP learning algorithms for interval regression analysis. Four examples are provided to show the validity and applicability of the proposed SVIRNs.
23120363
Embedding fuzzy mechanisms and knowledge in box-type reinforcement learning controllers
In this paper, we report our study on embedding fuzzy mechanisms and knowledge into box-type reinforcement learning controllers. One previous approach for incorporating fuzzy mechanisms can only achieve one successful run out of nine tests compared to eight successful runs in a nonfuzzy learning control scheme. After analysis, the credit assignment problem and the weighting domination problem are identified. Furthermore, the use of fuzzy mechanisms in temporal difference seems to play a negative factor. Modifications to overcome those problems are proposed. Furthermore, several remedies are employed in that approach. The effects of those remedies applied to our learning scheme are presented and possible variations are also studied. Finally, the issue of incorporating knowledge into reinforcement learning systems is studied. From our simulations, it is concluded that the use of knowledge for the control network can provide good learning results, but the use of knowledge for the evaluation network alone seems unable to provide any significant advantages. Furthermore, we also employ Makarovic's (1988) rules as the knowledge for the initial setting of the control network. In our study, the rules are separated into four groups to avoid the ordering problem.
16448441
A Hybrid Approach to Rendering Handwritten Characters
With the growing popularity of pen-based computers comes the need to display clear handwritten characters at small sizes on low-resolution displays. This paper describes a method for automatically constructing hinted TrueType fonts from on-line handwriting data. Hints add extra information to glyph outlines in the form of imperative constraints overriding side effects of the rasterization process. We use an aggressive matching strategy to find correspondences between an input glyph and a previously-hinted template, considering both global and local features to allow hinting even when they differ in shape and topology. Recognizing that stroke width statistics are among features that characterize a person’s handwriting, we recalculate global values in the control value table (CVT) before transfer to preserve the characteristics of the original handwriting.
17538957
The virtual panel architecture: A 3D gesture framework
The Virtual Panel Architecture (VPA) has been designed to help implement an intermediate abstraction with elements of both a physical control panel and a computer-based control panel. This abstraction, handling 3D point-based gesticulative interaction with modeled object hierarchies, is suitable for both virtual reality environments and traditional environments. Based on the VPA, an environment of virtual panels is demonstrated which can be incorporated into keyboard-and-mouse-based computing. Users are able to turn virtual knobs, adjust virtual sliders, and point at virtual screens on these virtual panels in this environment.<<ETX>>
43786937
Performance Analysis of Soft Handoff in CDMA Cellular Networks
The code-division multiple-access (CDMA) scheme has been considered as one possible choice of the future standards for cellular networks because of its various advantages. Since there can be only one carrier frequency being used in CDMA systems, a handoff scheme with diversity, a so-called "soft handoff", was proposed for higher communication quality and capacity. A mathematical model is developed to analyze the soft handoff process. Markov's concept is applied to describe the system's steady state statistical behavior. System performance such as blocking probability, handoff refused probability, and channel efficiency are also determined. It is concluded that the larger the area the soft handoff region is, the better users in the cellular network will feel.