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We present an algorithmic variant of the simplified fuzzy ARTMAP (SFAM) network, whose structure resembles those of feed-forward networks. Its difference with Kasuba's model is discussed, and their performances are compared on two benchmarks. We show that our algorithm is much faster than Kasuba's algorithm, and by increasing the number of training samples, the difference in speed grows enormously.The performances of the SFAM and the MLP (multilayer perceptron) are compared on three problems: the two benchmarks, and the Farsi optical character recognition (OCR) problem. For training the MLP two different variants of the backpropagation algorithm are used: the BPLRF algorithm (backpropagation with plummeting learning rate factor) for the benchmarks, and the BST algorithm (backpropagation with selective training) for the Farsi OCR problem.The results obtained on all of the three case studies with the MLP and the SFAM, embedded in their customized systems, show that the SFAM's convergence in fast-training mode, is faster than that of MLP, and online operation of the MLP is faster than that of the SFAM. On the benchmark problems the MLP has much better recognition rate than the SFAM. On the Farsi OCR problem, the recognition error of the SFAM is higher than that of the MLP on ill-engineered datasets, but equal on well-engineered ones. The flexible configuration of the SFAM, i.e. its capability to increase the size of the network in order to learn new patterns, as well as its simple parameter adjustment, remain unchallenged by the MLP. 相似文献
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The design of PID controllers for a Gryphon robot using four evolutionary algorithms: a comparative study 总被引:1,自引:0,他引:1
This paper compares performances of PID controllers designed for a Gryphon robot joints using four hybrid evolutionary algorithms.
We try to reduce the transient state of the step response. To this end, a function of some specifications of the step response
(i.e. overshoot, settling time, rise time and the steady state error) is defined. We minimize this function by using four
approaches, i.e. Particle swarm optimization (PSO) algorithm, the queen-bee (QB) algorithm, the genetic algorithm (GA) (all
hybridized with the Nelder-Mead (NM) algorithm), and the shuffled complex evolution (SCE), and the results are compared. For
designing a PID controller we should test several methods and based on the obtained results we can select one of them as the
best method. In our case, the queen-bee for joints 1, 2, and 4, the genetic algorithm for joint 3, and the shuffled complex
evolution method for joint 5 produce better results. 相似文献
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Memetic algorithms have been devised to rectify the absence of a local search mechanism in evolutionary algorithms. This paper proposes a differential memetic algorithm (DMA). To this end, first we propose a differential bidirectional random search as a local search algorithm. Then, a randomized blending crossover (RBleX) is proposed which aimed to scatter the new born offspring more diversely in the whole search space. We devise our proposed DMA, by using the RBleX crossover in the GA, and including the DBRS local search algorithm. A comparison of the performance of the DMA and those of seven other evolutionary/memetic or hybrid algorithms reported in two different papers on numerous bechmark functions demonstrates better performance of proposed DMA algorithm in most of the cases. 相似文献
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In this paper we present four discrete versions of two different existing honey bee optimization algorithms: the discrete
artificial bee colony algorithm (DABC) and three versions of the discrete fast marriage in honey bee optimization algorithm
(DFMBO1, DFMBO2, and DFMBO3). In these discretized algorithms we have utilized three logical operators, i.e. OR, AND and XOR
operators. Then we have compared performances of our algorithms and those of three other bee algorithms, i.e. the artificial
bee colony (ABC), the queen bee (QB), and the fast marriage in honey bee optimization (FMBO) on four benchmark functions for
various numbers of variables up to 100. The obtained results show that our discrete algorithms are faster than other algorithms.
In general, when precision of answer and number of variables are low, the difference between our new algorithms and the other
three algorithms is small in terms of speed, but by increasing precision of answer and number of variables, the needed number
of function evaluations for other algorithms increases beyond manageable amounts, hence their success rates decrease. Among
our proposed discrete algorithms, the DFMBO3 is always fast, and achieves a success rate of 100% on all benchmarks with an
average number of function evaluations not more than 1010. 相似文献
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