27,568 research outputs found
Assessment of Sustainable Development
The objective of this paper is to introduce fuzzy set theory and develop fuzzy mathematical models to assess sustainable development based on context-dependent economic, ecological, and societal sustainability indicators. Membership functions are at the core of fuzzy models, and define the degree to which indicators contribute to development. Although a decision-making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic propositions, to numerical data, expressed in measurements of sustainability indicators. In the future, practical implementation of such models will be based on elicitation of expert knowledge to construct a membership function. The fuzzy models developed in this paper provide a novel approach to support decisions regarding sustainable development.agriculture;assessment;fuzzy set theory;sustainable development
A Bibliography on Fuzzy Automata, Grammars and Lanuages
This bibliography contains references to papers on fuzzy formal languages, the generation of fuzzy languages by means of fuzzy grammars, the recognition of fuzzy languages by fuzzy automata and machines, as well as some applications of fuzzy set theory to syntactic pattern recognition, linguistics and natural language processing
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
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Learning fuzzy inference systems using an adaptive membership function scheme
An adaptive membership function scheme for general additive fuzzy systems is proposed in this paper. The proposed scheme can adapt a proper membership function for any nonlinear input-output mapping, based upon a minimum number of rules and an initial approximate membership function. This parameter adjustment procedure is performed by computing the error between the actual and the desired decision surface. Using the proposed adaptive scheme for fuzzy system, the number of rules can be minimized. Nonlinear function approximation and truck backer-upper control system are employed to demonstrate the viability of the proposed method
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