Model Order Reduction of Discrete-Time Systems Using Fuzzy C-Means Clustering
ثبت نشده
چکیده
A computationally simple approach of model order reduction for single input single output (SISO) and linear timeinvariant discrete systems modeled in frequency domain is proposed in this paper. Denominator of the reduced order model is determined using fuzzy C-means clustering while the numerator parameters are found by matching time moments and Markov parameters of high order system. Keywords—Model Order reduction, Discrete-time system, Fuzzy C-Means Clustering, Padé approximation.
منابع مشابه
OPTIMIZATION OF FUZZY CLUSTERING CRITERIA BY A HYBRID PSO AND FUZZY C-MEANS CLUSTERING ALGORITHM
This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find ago...
متن کاملUsing fuzzy c-means clustering algorithm for common lecturer timetabling among departments
University course timetabling problem is one of the hard problems and it must be done for each term frequently which is an exhausting and time consuming task. The main technique in the presented approach is focused on developing and making the process of timetabling common lecturers among different departments of a university scalable. The aim of this paper is to improve the satisfaction of com...
متن کاملOptimization and design of Adaptive Neuro-Fuzzy Inference System using Particle Swarm Optimization and Fuzzy C-Means Clustering to predict the scour after bucket spillway
Additionally, if the materials at downstream of bucket spillway are erodible, the ogee spillway is likely to overturn by the time. Therefore, the prediction of the scour after bucket spillway is pretty important. In this study, the scour depths at downstream of bucket spillway are modeled using a new meta-heuristic model. This model is developed by combination of the Adaptive Neuro-Fuzzy Infere...
متن کاملA Hybrid Time Series Clustering Method Based on Fuzzy C-Means Algorithm: An Agreement Based Clustering Approach
In recent years, the advancement of information gathering technologies such as GPS and GSM networks have led to huge complex datasets such as time series and trajectories. As a result it is essential to use appropriate methods to analyze the produced large raw datasets. Extracting useful information from large data sets has always been one of the most important challenges in different sciences,...
متن کاملPerformance Analysis and Comparision of Reduced Order Systems using Chebyshev polynomial, Improved Pole Clusteringand Fuzzy C-means Clustering Techniques
A mixed method for model order reduction of a linar, single input-single output systemis presented. The denominator of the original system is reduceedby using modified pole clustering techniques and fuzzy C-means clustering algorithm. The Chebyshev polynomial has been used for reducing the numerator of higher order transfer function.Then the result has been compared for boththe reduction techni...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013