Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate
TL;DR: VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability, and the simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS- RBF.
Abstract: The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF.
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TL;DR: An OBFs detector based on fast convergence wavelet artificial neural network (FC-W-ANN), which can detect abnormal magnetic signals under low SNR and has higher training accuracy and better stability is proposed.
10 citations
TL;DR: A novel control system to solve the deviation of system feedback by using dynamic lift and the mechanical decoupling method of fin hydrodynamic force and the lift measurement method based on double bearing load are proposed.
Abstract: For improving the control system of traditional ship fin stabilizers, this paper presents a novel control system to solve the deviation of system feedback by using dynamic lift. However, the difficulty of lift feedback control is lift measurement technology. Thus, the lift sensor is designed using Spoke-type structure and Wheatstone bridge-type strain conversion circuit. The design of compensation resistors is adopted in the circuit, which effectively reduces the effect of temperature on zero drift and sensitivity for the lift sensor. In addition, the mechanical decoupling method of fin hydrodynamic force and the lift measurement method based on double bearing load are proposed. In addition, then, the control system of ship fin stabilizer is improved in order to solve the main technical problems. Finally, the experimental results demonstrate that the designed sensors meet the requirements of lift measurement. In addition, the simulation results of the ship at different situations show that the effect of roll stabilization (86.803%–93.858%) is improved effectively.
6 citations
Cites background from "Evaluation for Sortie Generation Ca..."
...The research of control strategy is mainly focused on [2], [5], [18], [21], and [22]....
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TL;DR: A novel Takagi-Sugeno fuzzy robust control (NTSFRC) is designed, which keeps cost and inventory low and robust stability is guaranteed and part is supplied in time under a low cost in comparation with robust H∞ control strategy with particle swarm optimization.
4 citations
Journal Article•
TL;DR: The results show that promoting the capability of flight deck is the key factor to increase the number of sorties and can provide reference and quantization basis for improving the capacity of sortie generation and operation efficiency for carrier-borne aircraft.
Abstract: The operational capability of flight deck is the critical factor to affect the sortie generation of carrier-based aircraft, including launch operation, recovery and respot operations, serving and so on The definition of optimized flight deck operation plan was given A method to calculate the number of sorties generation in optimized flight deck operation plan was proposed Some factors including aircraft number, launch time, respotted time, recovery time how to affect the sortie generation were analyzed Finally, a type example was given The results show that promoting the capability of flight deck is the key factor to increase the number of sorties The results can provide reference and quantization basis for improving the capacity of sortie generation and operation efficiency for carrier-borne aircraft
3 citations
TL;DR: In this article, a customized FP-Growth implementation tailored to the requirements of SPADE was proposed, which significantly accelerates pattern mining and result filtering, and the energy consumption was reduced by up to two orders of magnitude.
Abstract: The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85 to 90 % of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.
2 citations
References
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TL;DR: A network that allocates a new computational unit whenever an unusual pattern is presented to the network, which learns much faster than do those using backpropagation networks and uses a comparable number of synapses.
Abstract: We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The units in this network respond to only a local region of the space of input values. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated that corrects the response to the presented pattern. If the network performs well on a presented pattern, then the network parameters are updated using standard LMS gradient descent. We have obtained good results with our resource-allocating network (RAN). For predicting the Mackey-Glass chaotic time series, RAN learns much faster than do those using backpropagation networks and uses a comparable number of synapses.
1,403 citations
01 Jun 2003
TL;DR: This paper proposes a novel separability-correlation measure (SCM) to rank the importance of attributes and significantly reduces the structural complexity of the RBF network and improves the classification performance.
Abstract: For high dimensional data, if no preprocessing is carried out before inputting patterns to classifiers, the computation required may be too heavy. For example, the number of hidden units of a radial basis function (RBF) neural network can be too large. This is not suitable for some practical applications due to speed and memory constraints. In many cases, some attributes are not relevant to concepts in the data at all. In this paper, we propose a novel separability-correlation measure (SCM) to rank the importance of attributes. According to the attribute ranking results, different attribute subsets are used as inputs to a classifier, such as an RBF neural network. Those attributes that increase the validation error are deemed irrelevant and are deleted. The complexity of the classifier can thus be reduced and its classification performance improved. Computer simulations show that our method for attribute importance ranking leads to smaller attribute subsets with higher accuracies compared with the existing SUD and Relief-F methods. We also propose a modified method for efficient construction of an RBF classifier. In this method we allow for large overlaps between clusters corresponding to the same class label. Our approach significantly reduces the structural complexity of the RBF network and improves the classification performance.
307 citations
TL;DR: Experimental results show that the FS-RBFNN can be used to design an RBF structure which has fewer hidden neurons; the training time is also much faster and the algorithm is applied for predicting water quality in the wastewater treatment process.
200 citations
TL;DR: The optimized approximation algorithm is proposed to address the overfitting problem in function approximation using neural networks (NNs) by means of a novel and effective stopping criterion based on the estimation of the signal-to-noise-ratio figure (SNRF).
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129 citations
TL;DR: A novel hybrid forward algorithm for the construction of radial basis function (RBF) neural networks with tunable nodes is proposed, leading to significantly improved network performance and reduced memory usage for the network construction.
Abstract: This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness
91 citations