Neural Network Model Identification Based on the Subtractive Clustering Method

نویسندگان

  • Haralambos Sarimveis
  • Alex Alexandridis
  • George Bafas
چکیده

A new algorithm for training radial basis function neural networks is presented in this paper. The algorithm, which is based on the subtractive clustering technique, has a number of advantages compared to the traditional learning algorithms, including faster training times and more accurate predictions. Due to these advantages the method proves suitable for developing discrete-time models for complex dynamical systems. Copyright © 2002 IFAC

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm

Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  First,...

متن کامل

Application of Soft Computing Methods for the Estimation of Roadheader Performance from Schmidt Hammer Rebound Values

Estimation of roadheader performance is one of the main topics in determining the economics of underground excavation projects. The poor performance estimation of roadheader scan leads to costly contractual claims. In this paper, the application of soft computing methods for data analysis called adaptive neuro-fuzzy inference system- subtractive clustering method (ANFIS-SCM) and artificial  neu...

متن کامل

Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm

Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  First,...

متن کامل

Multivariate Estimation of Rock Mass Characteristics Respect to Depth Using ANFIS Based Subtractive Clustering- Khorramabad- Polezal Freeway Tunnels

Combination of Adoptive Network based Fuzzy Inference System (ANFIS) and subtractive clustering (SC) has been used for estimation of deformation modulus (Em) and rock mass strength (UCSm) considering depth of measurement. To do this, learning of the ANFIS based subtractive clustering (ANFISBSC) was performed firstly on 125 measurements of 9 variables such as rock mass strength (UCSm), deformati...

متن کامل

Investment Decision-Making about Portfolio of Technology Development Projects; Based on the Analysis of Success Criteria using Fuzzy Neural Network and MADM

Technology development project is a type of investment project and it is important to identify the performance indicators and planning for the correct investment. The purpose of this research is the development of indicators of portfolio success, accurate analysis of the effects of indicators on each other and the achievement of a proper investment model. In this research, the success criteria ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2002