A Fuzzy Neural Tree for Possibilistic Reliability

An innovative neural fuzzy system is considered for possibilistic reliability using a neural tree structure with nodes of neuronal type. The total tree structure works effectively as a fuzzy logic system where the possibility theory plays important role with Gaussian possibility distribution at the nodes. The structure of the tree is determined by domain knowledge and each node represents a component of the system of concern. The reliabilities of the nodes are dependent on the reliabilities of the preceding nodes. The relationships among the nodes form the core of possibilistic reliability. For each input reliability composition the status of the system is known and interpreted not only at the system output but also at the granulated level at the system sub-domains which are represented by node outputs. The research is described in detail and a demonstrative computer experiment is reported.Architecture and The Built Environmen

Multiresolutional filter application for spatial information fusion in robot navigation

Building TechnologyArchitectur

Data sensor fusion for autonomous robotics

Building TechnologyArchitectur

Knowledge management by information mining

Architectur

Multiobjective Optimization for Cognitive Design

An innovative neural fuzzy system is considered for cognitive design using a neural tree structure with nodes of neuronal type, where Gaussian function plays the role of membership function. The total tree structure effectively works as a fuzzy logic system. The structure of the tree is determined by domain knowledge and each node represents an entity of the domain of concern. The states of these entities are dependent on the stimuli at the input and the relationships between the stimuli and the states form the core for cognitive design. Namely, for each stimulus the status of the system is known and interpreted not only at the output but also at the granulated level concerning the system sub-domains. The research is described in detail and demonstrative applications are reported.Architecture and The Built Environmen

The analytical hierarchy process applied for design analysis

Being an intelligent activity, design is a complex process to accomplish. The complexity stems from the elusive character of this activity, which cannot be explained in precise terms, in general. In a design process, the determined relationships among the design elements provide important information to understand the role of each element with respect to others thereby improving the design. For this aim the method of analytical hierarch process (AHP) is employed which provides hierarchical priorities of the design elements with respect to the parsed design goal. The priority information is extended to establish hierarchical relations among the elements as a novel approach to employ in architectural design process.Architectural Engineering +TechnologyArchitecture and The Built Environmen

Visual Perception with Color for Architectural Aesthetics

Studies on computer-based visual perception and aesthetical judgment for architectural design are presented. In the model, both color and the geometric aspects of human vision are jointly taken into account, quantifying the perception of an individual object, as well as a scene consisting of several objects. This is accomplished by fuzzy neural tree processing. Based on the perception model, aesthetical color compositions are identified for a scene using multi-objective evolutionary algorithm. The methodology is described together with associated computer experiments verifying the theoretical considerations. Modeling of aesthetical judgment is a significant step forapplications, where human-like visual perception and cognition are of concern. Examples of such applications are architectural design, product design, and urbanism.Accepted Author ManuscriptDesign Informatic

Precision Constrained Optimization by Exponential Ranking

Demonstrative results of a probabilistic constraint handling approach that is exclusively using evolutionary computation are presented. In contrast to other works involving the same probabilistic considerations, in this study local search has been omitted, in order to assess the necessity of this deterministic local search procedure in connection with the evolutionary one. The precision stems from the non-linear probabilistic distance measure that maintains stable evolutionary selection pressure towards the feasible region throughout the search, up to micro level in the range of 10-10 or beyond. The details of the theory are revealed in another paper [1]. In this paper the implementation results are presented, where the non-linear distance measure is used in the ranking of the solutions for effective tournament selection. The test problems used are selected from the existing literature. The evolutionary implementation without local search turns out to be already competitively accurate with sophisticated and accurate state-of-the-art constrained optimization algorithms. This indicates the potential for enhancement of the sophisticated algorithms, as to their precision and accuracy, by the integration of the proposed approach.Accepted Author ManuscriptDesign Informatic

A Fuzzy-Neural Tree Knowledge Model for the Assessment of Building’s Transformation

One building is more flexible in terms of use than the other and to determine how much a building ‘X’ is more flexible than a building ‘Y’ is a rather complex task. This research focuses on houses for the elderly in terms of future use, since the requirements have changed and many of the existing buildings do not meet new requirements. To asses a transformation of a building one needs to take many aspects into account such as: spatial transformation, technical transformation and their various sub-aspects. There are also different future use scenarios, defined by Netherlands Board for Healthcare Institutions, and one scenario is more suitable for a building than another. Firstly, in order to deal with this complex topic there is a need for a systematic approach where all relevant aspects determining a transformation value of a building will be defined. Thereafter, fuzzy-neural tree structure is used as a suitable method for knowledge representation and knowledge modeling.Architecture and The Built Environmen

Computational Cognitive Color Perception

Comprehension of aesthetical color characteristics based on a computational model of visual perception and color cognition are presented. The computational comprehension is manifested by the machine’s capability of instantly assigning appropriate colors to the objects perceived. They form a scene with aesthetically pleasing characteristics. The present approach to computational cognition is principally the same as contrived earlier [1]. This work distinguishes itself from the earlier work through the involvement of color differences. The color difference computations are carried out based on a standard human color observer model. The color difference information iscombined with geometric perception information using the method of fuzzy neural tree based on likelihood. The study exemplifies the suitability of the computational cognition for modeling cognition phenomenon. Cognitive color perception in computational form has generic relevance to applications involving human-like aesthetical appreciation, as is the case in building architecture, for instance and other design tasks.Accepted Author ManuscriptDesign Informatic