A self-organizing neural network is proposed which is inherently a fuzzy inference system with the capability of learning fuzzy rules from data. The learning strategy consists of two phases: a self-organizing clustering to establish the structure of the network as well as the initial values of its parameters and a supervised learning phase for optimal adjustment of these parameters. After learn...

We comment on a recent article by Newton (Environ. Model. Softw. (2010), 25, 15-23), which proposed a method, based on a Bayesian belief networks, for classifying the threat status of species under the IUCN Red List Categories and Criteria, and compared this method to an earlier one that we had developed that is based on fuzzy logic. There are three types of differences between the results of t...

This paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. Our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. This architecture of articial neural networks, can get a real input vector and calculates its corresponding fuzzy o...

To provide timely and appropriate assistance, robots must have the capability of proactively understanding a user’s personal needs, the so-called human intention inference. In human–human interaction, humans have a natural and implicit way to infer others’ intentions by selecting correlated context features and interpreting these features based on their life experience. However, robots do not h...

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...