In general, B?Reps model is used for three dimensional model creation. In recent years, the octree data structure gets into the spotlight as the computer technology makes rapid progress. Although several conversion algorithms of B?Reps into the octree data structure were proposed, former methods could not apply to B?Reps models with a concave shape. This paper describes a new conversion approach of a concave polyhedron into the octree data structure model. The proposed method consists of four processes, dividing a concave polygon into several convex polygons, determining whether octants intersect polygons, searching inner octants and calculating the volume occupied by a B?Reps object. Several experimental results show that the method is effective for the concave polyhedron and high conversion accuracy is obtained.
In this paper invariant zeros of distributed parameter systems are analyzed. First it is shown that the invariant zeros of the systems are given by the inverse of the eigenvalues of certain bounded operators. Next in the case where sensors and actuators are collocated in some sense, simple useful conditions are given which assure that the systems (not necessarily stable) have no unstable invariant zeros. Several examples are presented to show the usefulness of the conditions.
Two types of new adaptive generalized predictive control (GPC) system design methods are proposed for a class of non-linear systems described by Hammerstein model. These methods consist of two parts, that is, non-linear system identification part and GPC design one. Since Hammerstein model is a serial connection of static non-linear block and dynamic linear one; firstly we identify each characteristics based on observed input-output data. Then, two types of GPC design methods are applied to the identified model. One method is based on decomposition of the system to non-linear part and linear one. The GPC is designed for the latter part and actual input is computed in consideration of the former part. The other method strictly designs the GPC for the whole non-linear system by using the optimization technique. Effectiveness of the methods is examined through numerical examples.
For approximating nonlinear mappings, especially interpolating the datapoints in a high dimensional space, RBF networks with adaptive learning algorithm are attracting a great deal of interest due to their rapid training, generality and simplicity. RBF network is interpreted as three-layered neural networks and/or fuzzy reasoning rules. In this paper, we propose a neuro-fuzzy method to find a solution of partial differential equations. The elements in Finite Element Method (FEM) are replaced by fuzzy subsets defined by Gaussian membership functions, and the adaptive learning method is employed. For almost all problems in practical engineering where FEM is applied, the analytical solutions are not obtained. Hence, the approximate solution of FEM needs some tools for error estimation. So a method of error estimation based on the neuro-fuzzy scheme is also proposed.
In the identification of system whose a priori information of structure is poor, adequate candidate models are difficultly set up. This paper proposes the system identification method in which the object-oriented inference engine may be applied to search for the optimal model. The candidate models are generated one by one by using the search tree, and are evaluated by Bayesian theorem in consideration of the reliability of the a posteriori probability. Various simulation show that the model of true structure is discriminated clearly from the others as data increase. The system described by a functional expansion may be identified, because the flexible search of models may be possible on the basis of hierarchical inference.