29,197 research outputs found
An artificial immune systems based predictive modelling approach for the multi-objective elicitation of Mamdani fuzzy rules: a special application to modelling alloys
In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani Fuzzy Rule-Based Systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability
An algorithm for the solution of dynamic linear programs
The algorithm's objective is to efficiently solve Dynamic Linear Programs (DLP) by taking advantage of their special staircase structure. This algorithm constitutes a stepping stone to an improved algorithm for solving Dynamic Quadratic Programs, which, in turn, would make the nonlinear programming method of Successive Quadratic Programs more practical for solving trajectory optimization problems. The ultimate goal is to being trajectory optimization solution speeds into the realm of real-time control. The algorithm exploits the staircase nature of the large constraint matrix of the equality-constrained DLPs encountered when solving inequality-constrained DLPs by an active set approach. A numerically-stable, staircase QL factorization of the staircase constraint matrix is carried out starting from its last rows and columns. The resulting recursion is like the time-varying Riccati equation from multi-stage LQR theory. The resulting factorization increases the efficiency of all of the typical LP solution operations over that of a dense matrix LP code. At the same time numerical stability is ensured. The algorithm also takes advantage of dynamic programming ideas about the cost-to-go by relaxing active pseudo constraints in a backwards sweeping process. This further decreases the cost per update of the LP rank-1 updating procedure, although it may result in more changes of the active set that if pseudo constraints were relaxed in a non-stagewise fashion. The usual stability of closed-loop Linear/Quadratic optimally-controlled systems, if it carries over to strictly linear cost functions, implies that the saving due to reduced factor update effort may outweigh the cost of an increased number of updates. An aerospace example is presented in which a ground-to-ground rocket's distance is maximized. This example demonstrates the applicability of this class of algorithms to aerospace guidance. It also sheds light on the efficacy of the proposed pseudo constraint relaxation scheme
Expander -Decoding
We introduce two new algorithms, Serial- and Parallel- for
solving a large underdetermined linear system of equations when it is known that has at most
nonzero entries and that is the adjacency matrix of an unbalanced left
-regular expander graph. The matrices in this class are sparse and allow a
highly efficient implementation. A number of algorithms have been designed to
work exclusively under this setting, composing the branch of combinatorial
compressed-sensing (CCS).
Serial- and Parallel- iteratively minimise by successfully combining two desirable features of previous CCS
algorithms: the information-preserving strategy of ER, and the parallel
updating mechanism of SMP. We are able to link these elements and guarantee
convergence in operations by assuming that the signal
is dissociated, meaning that all of the subset sums of the support of
are pairwise different. However, we observe empirically that the signal need
not be exactly dissociated in practice. Moreover, we observe Serial-
and Parallel- to be able to solve large scale problems with a larger
fraction of nonzeros than other algorithms when the number of measurements is
substantially less than the signal length; in particular, they are able to
reliably solve for a -sparse vector from expander
measurements with and up to four times greater than what is
achievable by -regularization from dense Gaussian measurements.
Additionally, Serial- and Parallel- are observed to be able to
solve large problems sizes in substantially less time than other algorithms for
compressed sensing. In particular, Parallel- is structured to take
advantage of massively parallel architectures.Comment: 14 pages, 10 figure
Stackelberg Game for Distributed Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks
In this paper, we study the transmission strategy adaptation problem in an
RF-powered cognitive radio network, in which hybrid secondary users are able to
switch between the harvest-then-transmit mode and the ambient backscatter mode
for their communication with the secondary gateway. In the network, a monetary
incentive is introduced for managing the interference caused by the secondary
transmission with imperfect channel sensing. The sensing-pricing-transmitting
process of the secondary gateway and the transmitters is modeled as a
single-leader-multi-follower Stackelberg game. Furthermore, the follower
sub-game among the secondary transmitters is modeled as a generalized Nash
equilibrium problem with shared constraints. Based on our theoretical
discoveries regarding the properties of equilibria in the follower sub-game and
the Stackelberg game, we propose a distributed, iterative strategy searching
scheme that guarantees the convergence to the Stackelberg equilibrium. The
numerical simulations show that the proposed hybrid transmission scheme always
outperforms the schemes with fixed transmission modes. Furthermore, the
simulations reveal that the adopted hybrid scheme is able to achieve a higher
throughput than the sum of the throughput obtained from the schemes with fixed
transmission modes
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