Parameter Estimation via Analysis of Fuzzy Clusters (PEAF): An Algorithm to Estimate Parameters of Agent-Based Models
نویسندگان
چکیده
Biologically focused, agent-based models need many parameters in order to simulate system dynamics. It is often essential to explore the consequences of many parameter vectors before satisfactorily representing phenomena. In this work we propose a simple algorithm based on fuzzy clustering to estimate model parameter values for new situations utilizing the characteristics of previously simulated conditions. The estimated parameters can be used to predict the behavior of the system in a new situation. Using limited data, we successfully applied the algorithm to estimate parameter values of an agent-based model of hepatocytes (liver cells). Predictions provide acceptable correlations with observed values (p < 0.05, R 2 = 0.65).
منابع مشابه
A NEW APPROACH FOR PARAMETER ESTIMATION IN FUZZY LOGISTIC REGRESSION
Logistic regression analysis is used to model categorical dependent variable. It is usually used in social sciences and clinical research. Human thoughts and disease diagnosis in clinical research contain vagueness. This situation leads researchers to combine fuzzy set and statistical theories. Fuzzy logistic regression analysis is one of the outcomes of this combination and it is used in situa...
متن کاملApplication 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 کاملImprovement of density-based clustering algorithm using modifying the density definitions and input parameter
Clustering is one of the main tasks in data mining, which means grouping similar samples. In general, there is a wide variety of clustering algorithms. One of these categories is density-based clustering. Various algorithms have been proposed for this method; one of the most widely used algorithms called DBSCAN. DBSCAN can identify clusters of different shapes in the dataset and automatically i...
متن کاملAdaptive neuro-fuzzy inference system and neural network in predicting the size of monodisperse silica and process optimization via simulated annealing algorithm
In this study, Back-propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied to estimate the particle size of silica prepared by sol-gel technique. Simulated annealing algorithm (SAA) employed to determine the optimum practical parameters of the silica production. Accordingly, the process parameters, i.e. tetraethyl orthosilicate (TEOS), H2O and N...
متن کاملADAPTIVE NEURO FUZZY INFERENCE SYSTEM BASED ON FUZZY C–MEANS CLUSTERING ALGORITHM, A TECHNIQUE FOR ESTIMATION OF TBM PENETRATION RATE
The tunnel boring machine (TBM) penetration rate estimation is one of the crucial and complex tasks encountered frequently to excavate the mechanical tunnels. Estimating the machine penetration rate may reduce the risks related to high capital costs typical for excavation operation. Thus establishing a relationship between rock properties and TBM pe...
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 2007