Related Experiment Videos
Novel hysteretic noisy chaotic neural network for broadcast scheduling problems in packet radio networks
1Qiqihar University, Heilongjiang Province, China. snogisunming@yahoo.com.cn
Abstract:
Noisy chaotic neural network (NCNN), which can exhibit stochastic chaotic simulated annealing (SCSA), has been proven to be a powerful tool in solving combinatorial optimization problems. In order to retain the excellent optimization property of SCSA and improve the optimization performance of the NCNN using hysteretic dynamics without increasing network parameters, we first construct an equivalent model of the NCNN and then control noises in the equivalent model to propose a novel hysteretic noisy chaotic neural network (HNCNN). Compared with the NCNN, the proposed HNCNN can exhibit both SCSA and hysteretic dynamics without introducing extra system parameters, and can increase the effective convergence toward optimal or near-optimal solutions at higher noise levels. Broadcast scheduling problem (BSP) in packet radio networks (PRNs) is to design an optimal time-division multiple-access (TDMA) frame structure with minimal frame length, maximal channel utilization, and minimal average time delay. In this paper, the proposed HNCNN is applied to solve BSP in PRNs to demonstrate its performance. Simulation results show that the proposed HNCNN with higher noise amplitudes is more likely to find an optimal or near-optimal TDMA frame structure with a minimal average time delay than previous algorithms.
Related Concept Videos
Linear time-invariant Systems
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
Propagation of Uncertainty from Random Error
Neuronal Communication
BIBO stability of continuous and discrete -time systems
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Network Function of a Circuit
Basic Continuous Time Signals
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...