2,256 research outputs found
A Nonlinear System Identification Method Based on Adaptive Neural Network
Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields
What do we pay for asymmetric information? The evolution of mechanisms in online markets
The appearance of the Internet reduces transaction costs greatly, and brings the boom of online markets. While we are trying to regard it as the most realistic approximation of perfect competition market, the asymmetric information and a series of problems caused by it stop us from dreaming. As the old saying goes, there is no free lunch. This summer witnessed the collapse of the reputation system in Taobao, the biggest online transaction website in China. In fact, during the evolution of mechanisms in online markets, reputation, punishment and barriers to entry have been established in turn. What do we pay for maintaining these mechanisms? In which circumstance will they be eļ¬ective? In this paper I try to build a series of models within the principal-agent frame- work and repeated games to explain why and what we should pay for asymmetric information while enjoying shopping online. Specifically, these mechanisms are considered step by step and their boundary validation conditions are discussed. Finally, as the conclusion indicates, the more range that a mechanism is eļ¬ective, the more opportunity cost should be paid as a rent for information.online market; mechanism design; reputation;
Smaller SDP for SOS Decomposition
A popular numerical method to compute SOS (sum of squares of polynomials)
decompositions for polynomials is to transform the problem into semi-definite
programming (SDP) problems and then solve them by SDP solvers. In this paper,
we focus on reducing the sizes of inputs to SDP solvers to improve the
efficiency and reliability of those SDP based methods. Two types of
polynomials, convex cover polynomials and split polynomials, are defined. A
convex cover polynomial or a split polynomial can be decomposed into several
smaller sub-polynomials such that the original polynomial is SOS if and only if
the sub-polynomials are all SOS. Thus the original SOS problem can be
decomposed equivalently into smaller sub-problems. It is proved that convex
cover polynomials are split polynomials and it is quite possible that sparse
polynomials with many variables are split polynomials, which can be efficiently
detected in practice. Some necessary conditions for polynomials to be SOS are
also given, which can help refute quickly those polynomials which have no SOS
representations so that SDP solvers are not called in this case. All the new
results lead to a new SDP based method to compute SOS decompositions, which
improves this kind of methods by passing smaller inputs to SDP solvers in some
cases. Experiments show that the number of monomials obtained by our program is
often smaller than that by other SDP based software, especially for polynomials
with many variables and high degrees. Numerical results on various tests are
reported to show the performance of our program.Comment: 18 page
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