نتایج جستجو برای: subtractive clustering
تعداد نتایج: 105490 فیلتر نتایج به سال:
Recently, clustering algorithms combined conventional methods and artificial intelligence. FSCSOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster ...
در این پایان نامه ابتدا با استفاده از شبکه عصبی پرسپترون چند لایه با ساختارهای بهینهی حاصل شده از سعی و خطا جریان متوسط ماهانه حوزه لیقوان در قالب مدل بارش-جریان محاسبه شده است. سپس، از مدل نروفازی (anfis) به منظور بهبود عملکرد مدلهای آموزشی بهره گرفته شده است. شایان ذکر است در مدل انفیس تعیین ساختار فازی اولیه نقش مهمی را ایفا مینماید؛ در این راستا روشهای کلاسه بندی متداول شاملfuz...
Maximum surface settlement (MSS) is an important parameter for the design and operation of earth pressure balance (EPB) shields that should determine before operate tunneling. Artificial intelligence (AI) methods are accepted as a technology that offers an alternative way to tackle highly complex problems that can’t be modeled in mathematics. They can learn from examples and they are able...
The basic concept of the subtractive clustering algorithm is to choose a data point that has highest density (potential) in space (variable) as center cluster. number and position cluster centers formed are influenced by given radius (r) parameter value. If value very small, it will result neglect potential points around too large, increases contribution all points, thereby canceling effect den...
Software defects detection is one of the most important challenges of software development and it is the most prohibitive process in software development. The early detection of fault-prone modules helps software project managers to allocate the limited cost, time, and effort of developers for testing the defect-prone modules more intensively. In this paper, according to the importance of soft...
Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First...
Fault diagnosis is essential for the reliable, safe, and efficient operation of the plant and for maintaining quality of the products in industrial system. This paper presents an ensemble fault diagnosis algorithm based on fuzzy c-means algorithm (FCM) with the Optimal Number of Clusters (ONC) and probabilistic neural network (PNN), called FCM-ONC-PNN. In clustering methods, the estimation of t...
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