47,685 research outputs found
On structuring the rules of a fuzzy controller
Since the pioneering work of Zadeh and Mamdani and Assilian, fuzzy logic control has emerged as one of the most active and fruitful research areas. The applications of fuzzy logic control can be found in many fields such as control of stream generators, automatic train operation systems, elevator control, nuclear reactor control, automobile transmission control, etc. In this paper, two new structures of hierarchical fuzzy rule-based controller are proposed to reduce the number of rules in a complete rule set of a controller. In one approach, the overall system is split into sub-systems which are treated independently in parallel. A coordinator is then used to take into account the interactions. This is done via an iterating information exchange between the lower level and the coordinator level. From the point of view of information used, this structure is very similar to central structure in that the coordinator can have at least in principle, all the information that the local controllers have
An architecture for designing fuzzy logic controllers using neural networks
Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system
Hierarchical robust fuzzy sliding mode control for a class of simo under-actuated systems with mismatched uncertainties
The development of the algorithms for single input multi output (SIMO) under-actuated systems with mismatched uncertainties is important. Hierarchical sliding-mode controller (HSMC) has been successfully employed to control SIMO under-actuated systems with mismatched uncertainties in a hierarchical manner with the use of sliding mode control. However, in such a control scheme, the chattering phenomenon is its main disadvantage. To overcome the above disadvantage, in this paper, a new compound control scheme is proposed for SIMO under-actuated based on HSMC and fuzzy logic control (FLC). By using the HSMC approach, a sliding control law is derived so as to guarantee the stability and robustness under various environments. The FLC as the second controller completely removes the chattering signal caused by the sign function in the sliding control law. The results are verified through theoretical proof and simulation software of MATLAB through two systems Pendubot and series double inverted pendulum
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
Hierarchical fuzzy control for C-axis of CNC turning centers using genetic algorithms
A combined PD and hierarchical fuzzy control is proposed for the low-speed control of the C-axis of CNC turning centers considering the effects of transmission flexibility and complex nonlinear friction. Learning of the hierarchical structure and parameters of the suggested control strategy is carried out by using the genetic algorithms. The proposed algorithm consists of two phases: the first one is to search the best hierarchy, and the second to tune the consequent center values of the constituent fuzzy logic systems into the hierarchy. For the least total control rule number, the hierarchical fuzzy controller is chosen to include only the simple two-input/one-output fuzzy systems, and both binary and decimal genes are used for the selection, crossover and mutation of the genetic algorithm. The proposed approach is validated by the computer simulation. Each generation consists of 30 individuals: ten reproduced from its parent generation, ten generated by crossover, and the other ten by mutation. In the simulations, the C-axis is assumed to be driven by a vector-controlled AC induction motor, and the dynamic friction model suggested by Canudas de Wit et al. in 1995 is used
Advanced inference in fuzzy systems by rule base compression
This paper describes a method for rule base compression of fuzzy systems. The method compresses a fuzzy system with an arbitrarily large number of rules into a smaller fuzzy system by removing the redundancy in the fuzzy rule base. As a result of this compression, the number of on-line operations during the fuzzy inference process is significantly reduced without compromising the solution. This rule base compression method outperforms significantly other known methods for fuzzy rule base reduction.Peer Reviewe
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