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A new kind of fuzzy modeling method to construct an approximate solution to a differential equation is proposed in this paper. The proposed method is based on the interpolation mechanism of Rectangular Pyramid Fuzzy System (RPFS) and ends up with a solution to a nonlinear differential equation with variable coefficients. The proposed fuzzy modeling method is applied to model time-invariant second-order...
The node localization is one of the most important research issues in wireless sensor networks (WSNs). Traditionally, hop-count-based localization methods only take on integer value and have a same distance estimation to all of a node's one-hop neighbors. In this paper, a novel approach termed hop-count-based expectation of distance (HCED) algorithm is proposed. By partitioning an anchor's neighbor...
The node localization technique as a crucial technique that affects practicality, accuracy and effectiveness of the wireless sensor networks (WSNs). Sensor nodes are often deployed nonuniformly in anisotropic WSNs with holes in many applications. The existence of holes will affect the shortest distance between nodes and result in low accuracy of node localization. In this paper, an Extended Kalman...
In WSNs (Wireless Sensor Networks), a source node sends its packets via multi-hop relays to a destination node if the source node cannot communicate with the destination node directly. A destination's hop-count with respect to a source is the least number of multi-hop relays required for the source to reach it. Given a WSN whose nodes are distributed randomly according to a homogeneous Poisson point...
Localization is one of the most important research issues in wireless sensor networks (WSNs). Hop-count based localization has been proposed as a cost-effective alternative to the expensive hardware-based localization schemes. In this paper, we propose a new method to estimate distances between anchors and nodes based on hop-count information. Localization is then achieved using the estimated distances...
System identification is a very important part in control theory for nonlinear analysis and optimization. In the past years, neural identification of dynamic systems gains great interest because of its powerful mapping capability. In this paper, a learning algorithm for the feedforward neural network named extreme learning machine (ELM) is applied for nonlinear system identification problem. The simulation...
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