7,581 research outputs found
Model fusion using fuzzy aggregation: Special applications to metal properties
To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments
Macroeconomics modelling on UK GDP growth by neural computing
This paper presents multilayer neural networks used in UK gross domestic product estimation. These networks are trained by backpropagation and genetic algorithm based methods. Different from backpropagation guided by gradients of the performance, the genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then uses the analysed results to breed new networks that tend to be better suited to the problems in hand. It is shown that this guided evolution leads to globally optimal networks and more accurate results, with less adjustment of the algorithm needed
Causative factors of construction and demolition waste generation in Iraq Construction Industry
The construction industry has hurt the environment from the waste generated during
construction activities. Thus, it calls for serious measures to determine the causative
factors of construction waste generated. There are limited studies on factors causing
construction, and demolition (C&D) waste generation, and these limited studies only
focused on the quantification of construction waste. This study took the opportunity to
identify the causative factors for the C&D waste generation and also to determine the
risk level of each causal factor, and the most important minimization methods to
avoiding generating waste. This study was carried out based on the quantitative
approach. A total of 39 factors that causes construction waste generation that has been
identified from the literature review were considered which were then clustered into 4
groups. Improved questionnaire surveys by 38 construction experts (consultants,
contractors and clients) during the pilot study. The actual survey was conducted with
a total of 380 questionnaires, received with a response rate of 83.3%. Data analysis
was performed using SPSS software. Ranking analysis using the mean score approach
found the five most significant causative factors which are poor site management, poor
planning, lack of experience, rework and poor controlling. The result also indicated
that the majority of the identified factors having a high-risk level, in addition, the better
minimization method is environmental awareness. A structural model was developed
based on the 4 groups of causative factors using the Partial Least Squared-Structural
Equation Modelling (PLS-SEM) technique. It was found that the model fits due to the
goodness of fit (GOF ≥ 0.36= 0.658, substantial). Based on the outcome of this study,
39 factors were relevant to the generation of construction and demolition waste in Iraq.
These groups of factors should be avoided during construction works to reduce the
waste generated. The findings of this study are helpful to authorities and stakeholders
in formulating laws and regulations. Furthermore, it provides opportunities for future
researchers to conduct additional research’s on the factors that contribute to
construction waste generation
Portfolio selection using neural networks
In this paper we apply a heuristic method based on artificial neural networks
in order to trace out the efficient frontier associated to the portfolio
selection problem. We consider a generalization of the standard Markowitz
mean-variance model which includes cardinality and bounding constraints. These
constraints ensure the investment in a given number of different assets and
limit the amount of capital to be invested in each asset. We present some
experimental results obtained with the neural network heuristic and we compare
them to those obtained with three previous heuristic methods.Comment: 12 pages; submitted to "Computers & Operations Research
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
- …