Fuzzy logic integration into construction management planning

ENGLISH: A construction of an asset is challenging work associated with huge responsibilities. Project manager’s main challenges in construction are generally Time-Cost-Quality dependent. In addition to these factors, Project Managers or construction Manager have to deal with numerous uncertainties in the process of construction. Considering its diverse nature and activity performed in construction. Here major role playing factor in success of a project is efficient scheduling of the construction project. While scheduling a project, the factors which are considered are mostly tangible, such as resources, labor and capital. My main objective in this research is to reduce the duration of the activity involved in construction, while considering qualitative factors like site organization, Labor Skills and quality of equipment used and reveal the effect of these tangible factors on duration of the project. Since these factors are difficult to be measured using classical mathematic theories. We have opted Fuzzy Logic Theory as our base, which can measure mathematically with use of Fuzzy Logic Tool Box in MATLAB®. In order to implement the proposed technique, various membership functions need to be estimated using judgement and guidance of experts. One of the main advantage of the proposed technique is that it can be easily implemented in existing computer programs and thus having a possibility to schedule a project efficiently

Disseny i implementació d'una metodologia per a construir sistemes difusos clàssics de forma automàtica a partir de models FIR

En aquest projecte es desenvolupa una nova metodologia que actua com a extensió de la tècnica de FIR. D'una banda construeix de forma automàtica models FIS a partir de models FIR, d'altra banda ofereix la predicció del comportament de sistemes mitjançant un sistema d'inferència híbrid FIR + FISIn this project, a new methodology is developed that acts as an extension of the FIR technique. On one hand, it automatically builds FIS models from FIR models, on the other hand it offers the prediction of the behavior of systems using a FIR + FIS hybrid inference syste

Identification of chemical species using artificial intelligence to interpret optical emission spectra

The nonlinear modeling capabilities of artificial neural networks (ANN’s) are renowned in the field of artificial intelligence (Al) for capturing knowledge that can be very difficult to understand otherwise. Their ability to be trained on representative data within a particular problem domain and generalise over a set of data make them efficient predictive models. One problem domain that contains complex data that would benefit from the predictive capabilities of ANN’s is that of optical emission spectra (OES). OES is an important diagnostic for monitoring plasma species within plasma processing. Normally, OES spectral interpretation requires significant prior expertise from a spectroscopist. One way of alleviating this intensive demand in order to quickly interpret OES spectra is to interpret the data using an intelligent pattern recognition technique like ANN’s. This thesis investigates and presents MLP ANN models that can successfully classify chemical species within OES spectral patterns. The primary contribution of the thesis is the creation of deployable ANN species models that can predict OES spectral line sizes directly from six controllable input process parameters; and the implementation of a novel rule extraction procedure to relate the real multi-output values of the spectral line sizes to individual input process parameters. Not only are the trained species models excellent in their predictive capability, but they also provide the foundation for extracting comprehensible rules. A secondary contribution made by this thesis is to present an adapted fuzzy rule extraction system that attaches a quantitative measure of confidence to individual rules. The most significant contribution to the field of Al that is generated from the work presented in the thesis is the fact that the rule extraction procedure utilises predictive ANN species models that employ real continuously valued multi-output data. This is an improvement on rule extraction from trained networks that normally focus on discrete binary output

A Neuro-Expert Approach for Decision -Making in Welding Environment.

Decision making in welding is very important for achieving a good quality welded joint for the least possible cost. Of particular interest is decision making involving the selection of process, parameters, weld procedure specification, defect analysis and trouble shooting. This research has provided a means of capturing the planning knowledge in a Neuro-Expert System in a form that is capable of learning new information, correcting old information and automating the decision-making process in a welding environment. A strategy is formulated for the representation of knowledge in the form of a neural links and the translation of rules into neural link weights. After training those weights were converted back into rules to find out the inconsistent rules and capture new rules using a new approach. The various job variables affecting the process of welding are identified in detail and a Neuro-Expert system for the selection of process, parameters and weld procedure specification is developed. The neural networks are integrated with an expert system for decision making in welding environment. Apart from providing the initial parameters of welding, the expert system is used to validate the output of the neural network and served as a user-friendly interface for the neural network. Defect Analysis is performed in welding domain by mapping the welding parameters and defect patterns in a neural network. A neural network based approach for representing the knowledge in expert system is utilized for this purpose as the modification and updating of the knowledge was easier

Rule extraction for fuzzy modeling

[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]E