Based on the background of population development strategy, this paper uses grounded theory to take roots in the job interviews of two retired professors after returning to colleges and universities, and constructs a ...
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Based on the background of population development strategy, this paper uses grounded theory to take roots in the job interviews of two retired professors after returning to colleges and universities, and constructs a secondary development model for retired college professors. Studies have shown that family responsibilities, the "outdated theory" of retired professors, public and private schools, teachers, the establishment of rehiring platforms, and the acquisition of effective information are the key driving factors affecting the development of human resources for retired professors. In view of this, this article puts forward three countermeasures to build a platform for professors' re-employment, create information communication channels, and deepen college senior education, hoping to provide theoretical guidance and management inspiration for the secondary development of college retired professors.
A sliding window hybrid quasi Newton algorithm is proposed in this paper Tor minimum bit error rate nonlinear equalizers online training. Switching between sliding window stochastic gradient algorithm and sliding wind...
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A sliding window hybrid quasi Newton algorithm is proposed in this paper Tor minimum bit error rate nonlinear equalizers online training. Switching between sliding window stochastic gradient algorithm and sliding window quasi Newton algorithm makes the new algorithm be stable and converge fast Moreover, by modifying the quasi Newton method, the new algorithm can be applied to high-dimensional parameters. In simulations, the new algorithm is used for training nonlinear equalizers in direct sequence spread spectrum communications and its high efficiency is proved by simulation results.
An important Issue in design and implementation a neural network is that perturbations of training pattern pairs may cause some disadvantages to outputs. How the perturbations of training pattern pairs in Morpholo...
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ISBN:
(纸本)9781612842899
An important Issue in design and implementation a neural network is that perturbations of training pattern pairs may cause some disadvantages to outputs. How the perturbations of training pattern pairs in Morphological Bidirectional Associative Memories (MBAMs) influence on the outputs is discussed in this paper. We define the outputs' max error to evaluate the robustness of the MBAMs. The related theorem and example show that the MBAMs have good robustness on the perturbation of training pattern pairs. This is of important sense in MBAMs' practical applications, such as analysis of the networks' performances, selection or establishment of the learning algorithms and acquisition the training pattern pairs.
The application of traditional adaptive control algorithm usually depends on the precise mathematical model of process,but it is difficult to establish a mathematical model for a dynamic *** existing Model Free Adapti...
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The application of traditional adaptive control algorithm usually depends on the precise mathematical model of process,but it is difficult to establish a mathematical model for a dynamic *** existing Model Free Adaptive(MFA) control algorithm structure is based on Back Propagation(BP) Neural Network,which does not fully take into account the continuous characteristic relationship between the input timing error *** to this actual problem,an improved MFA control algorithm based on Gated Recurrent Unit(GRU) network is proposed in this paper,compared with BP network,GRU network has more advantages in processing timing *** fully considers the information of error sequence,and more appropriate manipulated variable could be generated automatically to satisfy the demand of an open-loop stable and controllable single-variable,multivariable or large delay process system without the need for complicated manual adjustments,quantitative knowledge of the process or the identifier of the controlled system and learning *** results demonstrate that the GRU-MFA learning algorithm performs better in terms of stability,response speed and adaptability than the BP-MFA algorithm without human intervention.
Various classifiers have sprung up in recent years. This paper introduces a new intelligent algorithm for text categorization based on improved random forest algorithm. This improvement greatly increases the performan...
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Various classifiers have sprung up in recent years. This paper introduces a new intelligent algorithm for text categorization based on improved random forest algorithm. This improvement greatly increases the performance of the original random forest algorithm. The classifier was tested on the reuters-21578 data set and its classification effect was obtained. The classifier is compared with traditional principle similar classifier CART, REPTree and J48. The experimental results show that the classification accuracy of text classifier based on improved random forest algorithm is higher, and it is faster.
To address the drawbacks of slow convergenceand low accuracy of Double Parallel Feed-forward ProcessNeural Networks with output as function, an Elman-stylefeedback process neural network model with output as function ...
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ISBN:
(纸本)9781424472352
To address the drawbacks of slow convergenceand low accuracy of Double Parallel Feed-forward ProcessNeural Networks with output as function, an Elman-stylefeedback process neural network model with output as function is proposed. The learning algorithm of this model is given and the effectiveness of this model is proved by a non-linear system identification problem.
With the development of artificial intelligence, the mainstream neural networks currently have many insurmountable problems, such as a large amount of calculation, high power consumption, and low intelligence. In orde...
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With the development of artificial intelligence, the mainstream neural networks currently have many insurmountable problems, such as a large amount of calculation, high power consumption, and low intelligence. In order to solve the above problems, the spiking neural network simulates "creating the brain" according to the characteristics of the human brain, and realizes intelligence with a neural network close to that of a living being. This article uses biological causality to creatively propose a multi-layered spiking neural network structure with universality, and a learning algorithm for spiking neural network, and apply it to poker games, so that the poker robot can learn a person's card ability The ultimate degree of personification was 85%, which verified the feasibility of the structure and algorithm of impulsive neural network.
Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical ...
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ISBN:
(纸本)9781467324199
Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for 1-hour in the future but this approach can be extended to predict more hours in the future as per the requirement (with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.
Two important challenges facing 5G are energy efficiency and mobile users' mobility in heterogeneous wireless networks (HetNets). One of the important techniques for improving energy efficiency is base station (BS...
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ISBN:
(纸本)9781479959532
Two important challenges facing 5G are energy efficiency and mobile users' mobility in heterogeneous wireless networks (HetNets). One of the important techniques for improving energy efficiency is base station (BS)'s switching between ON and OFF modes which allows the BS to turn off some its components in lower load situations. In this paper, we address user's seamless mobility problem and propose a handoff (HO) algorithm based on BS's estimated load. The proposed HO algorithm based on estimated load (PHA-EL) balances load by imposing HOs from highly loaded BSs to lightly loaded BSs. When a BS is overloaded, the user's quality of service (QoS) will degrade and therefore the PHA-EL is used to improve system throughput. The PHA-EL algorithm is combined with BSs which are able to switch between ON and OFF modes (PHA-EL/ON-OFF switching) in order to improve the energy efficiency of the system. Therefore, this algorithm achieves both energy- and spectral- efficiency. Simulation results indicate that the proposed algorithm yields better performance in terms of average number of HOs, average load per BS and average payoff per BS, compared to baseline algorithm.
This paper presents novel methodologies for face recognition: template-matching using Dynamic Time Warping (DTW) and Long-Short-Term-Memory (LSTM) neural network supervised classification. The advantage of the DTW alg...
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ISBN:
(纸本)9788022728560
This paper presents novel methodologies for face recognition: template-matching using Dynamic Time Warping (DTW) and Long-Short-Term-Memory (LSTM) neural network supervised classification. The advantage of the DTW algorithm is that it requires only one prototype (sample) for each class, that is, a single representative template is enough for classification purposes. The LSTM network is a novel recurrent network architecture that implements an appropriate gradient-based learning algorithm. It overcomes the vanishing-gradient problem. Experiments with images from the MIT-CBCL face recognition database provided good results for both approaches. For DTW, the obtained results indicate that the proposed method is robust against the presence of random noise on observations and templates, since it is capable to deal with unpredictable variations. The LSTM training achieved good performance even with small feature sets.
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