The Theory of Fuzzy Logic and its Application to Real Estate Valuation

Fuzzy logic is based on the central idea that in fuzzy sets each element in the set can assume a value from 0 to 1, not just 0 or 1, as in classic set theory. Thus, qualitative characteristics and numerically scaled measures can exhibit gradations in the extent to which they belong to the relevant sets for evaluation. This degree of membership of each element is a measure of the element’s "belonging" to the set, and thus of the precision with which it explains the phenomenon being evaluated. Fuzzy sets can be combined to produce meaningful conclusions, and inferences can be made, given a specified fuzzy input function. The article demonstrates the application of fuzzy logic to an income-producing property, with a resulting fuzzy set output.

Development of accident prediction model by using artificial neural network (ANN)

Statistical or crash prediction model have frequently been used in highway safety studies. They can be used in identify major contributing factors or establish relationship between crashes and explanatory accident variables. The measurements to prevent accident are from the speed reduction, widening the roads, speed enforcement, or construct the road divider, or other else. Therefore, the purpose of this study is to develop an accident prediction model at federal road FT 050 Batu Pahat to Kluang. The study process involves the identification of accident blackspot locations, establishment of general patterns of accident, analysis of the factors involved, site studies, and development of accident prediction model using Artificial Neural Network (ANN) applied software which named NeuroShell2. The significant of the variables that are selected from these accident factors are checked to ensure the developed model can give a good prediction results. The performance of neural network is evaluated by using the Mean Absolute Percentage Error (MAPE). The study result showed that the best neural network for accident prediction model at federal road FT 050 is 4-10-1 with 0.1 learning rate and 0.2 momentum rate. This network model contains the lowest value of MAPE and highest value of linear correlation, r which is 0.8986. This study has established the accident point weightage as the rank of the blackspot section by kilometer along the FT 050 road (km 1 – km 103). Several main accident factors also have been determined along this road, and after all the data gained, it has successfully analyzed by using artificial neural network

Pembangunan dan penilaian modul berbantukan komputer bagi subjek pemasaran : Politeknik Port Dickson

Kajian ini bertujuan membangunkan Modul Berbantukan Komputer (MBK) bagi subjek Pemasaran. MBK ini dibangunkan dengan menggunakan pensian AutoPlay Media dan Flash MX. Sampel kajian ini terdiri daripada 30 orang pelajar Diploma Pemasaran di Politeknik Port Dickson. Data dikumpulkan melalui kaedah soal selidik dan dianalisis berdasarkan kekerpan, peratusan dan skor min dengan menggunakan perisian Statistical Package For Social Sciene (SPSS) versi 11.0. Dapatan kajian menunjukkan penilaian terhadap pembagunan MBK di dalam proses P&P adalah tinggi. Ini bermakna MBK ini sesuai digunakan di Politeknik Port Dickson di dalam proses P&P

Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels

The Theory of Fuzzy Logic and its application to Real Estate Valuation

Fuzzy logic is based on the central idea that in fuzzy sets each element in the set can assume a value from 0 to 1, not just 0 or 1, as in classic set theory. Thus, qualitative characteristics and numerically scaled measures can exhibit gradations in the extent to which they belong to the relevant sets for evaluation. This degree of membership of each element is a measure of the element’s "belonging" to the set, and thus of the precision with which it explains the phenomenon being evaluated. Fuzzy sets can be combined to produce meaningful conclusions, and inferences can be made, given a specified fuzzy input function. The article demonstrates the application of fuzzy logic to an income-producing property, with a resulting fuzzy set output

Eliciting density ratio classes

AbstractThe probability distributions of uncertain quantities needed for predictive modelling and decision support are frequently elicited from subject matter experts. However, experts are often uncertain about quantifying their beliefs using precise probability distributions. Therefore, it seems natural to describe their uncertain beliefs using sets of probability distributions. There are various possible structures, or classes, for defining set membership of continuous random variables. The Density Ratio Class has desirable properties, but there is no established procedure for eliciting this class. Thus, we propose a method for constructing Density Ratio Classes that builds on conventional quantile or probability elicitation, but allows the expert to state intervals for these quantities. Parametric shape functions, ideally also suggested by the expert, are then used to bound the nonparametric set of shapes of densities that belong to the class and are compatible with the stated intervals. This leads to a natural metric for the size of the class based on the ratio of the total areas under upper and lower bounding shape functions. This ratio will be determined by the characteristics of the shape functions, the scatter of the elicited values, and the explicit expert imprecision, as characterized by the width of the stated intervals. We provide some examples, both didactic and real, and conclude with recommendations for the further development and application of the Density Ratio Class

Uncertainty in through-life costing-review and perspectives

Estimating through-life cost (TLC) is an area that is critical to many industrial sectors, and in particular, within the defense and aerospace where products are complex and have extended life cycles. One of the key problems in modeling the cost of these products is the limited life-cycle information at the early stage. This leads to epistemic and aleatory uncertainty within the estimation process in terms of data, estimation techniques, and scenarios analysis. This paper presents a review of the uncertainty classification in engineering literature and the nature of uncertainty in TLC estimation. Based on the review, the paper then presents a critique of the current uncertaintymodeling approaches in cost estimation and concludes with suggestion for the requirement of a different approach to handling uncertainty in TLC. The potential value of imprecise probability should be explored within the domain of TLC to assist cost estimators and decision makers in understanding and assessing the uncertainty. The implication of such a representation in terms of decision making under risk and decision making under uncertainty is also discussed

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments