Fuzzy Logic in Genetic Regulatory Network Models
نویسندگان
چکیده
منابع مشابه
Neural network, genetic, and fuzzy logic models of spatial interaction
The author investigates the extent to which smart computational methods can be used to create new and better performing types of spatial interaction model. He briefly describes the application of three different computationally intensive modelling technologies and compares the performance of the resulting models on a benchmark data set. It would appear that performance improvements of up to a f...
متن کاملNetwork growth models and genetic regulatory networks.
We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with the innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterin...
متن کاملTopology Control in Wireless Sensor Network using Fuzzy Logic
Network sensors consist of sensor nodes in which every node covers a limited area. The most common use ofthese networks is in unreachable fields.Sink is a node that collects data from other nodes.One of the main challenges in these networks is the limitation of nodes battery (power supply). Therefore, the use oftopology control is required to decrease power consumption and increase network acce...
متن کاملPredictions of Tool Wear in Hard Turning of AISI4140 Steel through Artificial Neural Network, Fuzzy Logic and Regression Models
The tool wear is an unavoidable phenomenon when using coated carbide tools during hard turning of hardened steels. This work focuses on the prediction of tool wear using regression analysis and artificial neural network (ANN).The work piece taken into consideration is AISI4140 steel hardened to 47 HRC. The models are developed from the results of experiments, which are carried out based on De...
متن کاملGenetic Fuzzy Logic Controllers
The conventional fuzzy logic controller (CFLC) is limited in application, because its logic rules and membership functions have to be preset with expert knowledge. To avoid such drawbacks, a genetic fuzzy logic controller (GFLC) is proposed by employing an iterative evolution algorithm to promote the learning performance of logic rules and the tuning effectiveness of membership functions from e...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computers Communications & Control
سال: 2009
ISSN: 1841-9836,1841-9836
DOI: 10.15837/ijccc.2009.4.2453