This paper presents a new parallel distributedprocessing (PDP) approach to solve job-shop scheduling problem which is np-complete. In this approach, a stochastic model and a controlled external energy is used to impr...
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ISBN:
(纸本)0780314212
This paper presents a new parallel distributedprocessing (PDP) approach to solve job-shop scheduling problem which is np-complete. In this approach, a stochastic model and a controlled external energy is used to improve the scheduling solution iteratively. Different to the processing element (PE) of the Hopfield neuralnetwork model, each PE of our model represents an operation of a certain job. So, the functions of each PE are a little more complicated than that of a Hopfield PE. Under such model, each PE is designed to perform some stochastic, collective computations. From experimental result, the solutions can be improved toward optimal ones much faster than other methods. Instead of the polynomial number of variables needed in neuralnetwork approach, the variables number needed to formulate a job-shop problem in our model is only a linear function of the operation number contained in the given job-shop problem.
It is difficult to build a strict mathematical model for WEDM due to the complication of the machining process and the nonlinear relation between process parameters and process targets. The neuralnetwork is suited to...
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ISBN:
(纸本)9783642319648
It is difficult to build a strict mathematical model for WEDM due to the complication of the machining process and the nonlinear relation between process parameters and process targets. The neuralnetwork is suited to the modeling of complex system, because it has the functions of self-organized, self-learning and associative memory, and properties of distributed parallel type and high robustness. Therefore, this paper attempts to use the RBF neuralnetwork for the process modeling of WEDM.
In this paper, we propose a new neuralnetwork model termed semantic and episodic associative neuralnetwork (SEANN) for natural language processing. The SEANN can deal with both semantic memory and episodic memory by...
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ISBN:
(纸本)0780379527
In this paper, we propose a new neuralnetwork model termed semantic and episodic associative neuralnetwork (SEANN) for natural language processing. The SEANN can deal with both semantic memory and episodic memory by sentences represented in a form of a semantic network. In this model, both semantic memory and episodic memory are represented in triples-representation of concepts. Our model consists of concepts of sentences associative neuralnetwork (CSANN) and MAM using area representation. CSANN can recall sentences in a form of triples-representation, and MA M using area representation can recall plural triples-representations from a word. We have carried out computer experiments to confirm the validity of the SEANN for natural language processing. We have investigated that our model can recall plural semantic memories from one word, and can recall semantic memories concerning with episodic memory.
Recent advances in deep learning for natural language processing achieve and improve over state of the art results in many natural language processing tasks. One problem with neuralnetwork models, however, is that th...
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ISBN:
(纸本)9789526839783
Recent advances in deep learning for natural language processing achieve and improve over state of the art results in many natural language processing tasks. One problem with neuralnetwork models, however, is that they require large datasets, including large labeled datasets for the corresponding problems. In this work, we suggest a data augmentation method based on extending a given dataset with synonyms for the words appearing there. We apply this approach to the morphologically rich Russian language and show improvements for modern neuralnetwork NLP models on standard tasks such as sentiment analysis.
Designing distributed energy system (DES) is a complex task due to large varieties and combinations of energy generation, conversion, and storage technologies as well as time-varying energy supplies and demands. In th...
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ISBN:
(纸本)9781467362498;9781467362481
Designing distributed energy system (DES) is a complex task due to large varieties and combinations of energy generation, conversion, and storage technologies as well as time-varying energy supplies and demands. In this article, an artificial neuralnetwork (ANN) is trained by known DES design samples. Results have shown that after training, ANN can approximate the complex DES mathematical model and yield similar new DES designs to the mathematical model, given new conditions of energy supplies and demands. The advantages of using ANN to design DES lie in the simple structure of ANN and the learning ability from practical as well as updated samples.
The apparent dichotomy between symbolic AI processing and distributedneuralprocessing cannot be absolute, since neuralnetworks that capture essential features of human intelligence will also model some of the symbo...
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ISBN:
(纸本)0818642009
The apparent dichotomy between symbolic AI processing and distributedneuralprocessing cannot be absolute, since neuralnetworks that capture essential features of human intelligence will also model some of the symbolic processes of which humans are capable. Indeed, a primary goal of biological neuralnetwork research is to design systems that can self-organize intelligent symbolic processing capabilities. One such system is the ARTMAP family of neuralnetworks. Most if not all of the purported dichotomies between traditional artificial intelligence and neuralnetwork research dissolve within these systems. Although ARTMAP systems are neuralnetworks, they are also a type of self-organizing production system capable of hypothesis testing and memory search. They embody continuous and discrete, parallel and serial, and distributed and localized properties. Their symbols are compressed, often digital representations, yet they are formed and stabilized through a process of resonant binding that is distributed across the system. They are used to explain and predict data on both the psychological and the neurobiological levels, yet their unique combinations of computational properties are also rapidly finding their way into technology. They are capable of autonomously discovering rules about the environments to which they adapt, yet these rules are emergent properties of network dynamics rather than formal algorithmic statements.
The aim of the paper is to present a parallel simulator that allows to use distributed algorithmic to develop artificial neuralnetworks. We have developed an efficient parallel simulator to implement artificial neura...
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ISBN:
(纸本)0780370449
The aim of the paper is to present a parallel simulator that allows to use distributed algorithmic to develop artificial neuralnetworks. We have developed an efficient parallel simulator to implement artificial neuralnetworks. This simulator makes use of the parallel properties of connectionist models to make an efficient parallel implementation onto general purpose shared memory MIMD computers. Therefore this simulator naturally leads to build (learning and generalising) neural algorithms that respect the large natural parallel aspects of these models.
We present a system for keyword spotting that, except for a front-end component for feature generation, it is entirely contained in a deep neuralnetwork (DNN) model trained "end-to-end" to predict the prese...
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ISBN:
(纸本)9781479981311
We present a system for keyword spotting that, except for a front-end component for feature generation, it is entirely contained in a deep neuralnetwork (DNN) model trained "end-to-end" to predict the presence of the keyword in a stream of audio. The main contributions of this work are, first, an efficient memoized neuralnetwork topology that aims at making better use of the parameters and associated computations in the DNN by holding a memory of previous activations distributed over the depth of the DNN. The second contribution is a method to train the DNN, end-to-end, to produce the keyword spotting score. This system significantly outperforms previous approaches both in terms of quality of detection as well as size and computation.
This paper is concerned with the global asymptotic stability analysis problem for a class of stochastic neuralnetworks with interval discrete and distributed delays. The parameter uncertainties are assumed to be norm...
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ISBN:
(纸本)9783642106767
This paper is concerned with the global asymptotic stability analysis problem for a class of stochastic neuralnetworks with interval discrete and distributed delays. The parameter uncertainties are assumed to be norm bounded. Based on Lyapunov-Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality(LMI) approach. It is also shown that the result in this paper cover sot tie recently published works. A numerical example is provided to demonstrate the usefulness of the proposed criteria.
A framework presenting a basic conceptual structure used to solve adaptive learning problems in soft real time applications is proposed. Its design consists of two supervised neuralnetworks running simultaneously. On...
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ISBN:
(纸本)9783642137686
A framework presenting a basic conceptual structure used to solve adaptive learning problems in soft real time applications is proposed. Its design consists of two supervised neuralnetworks running simultaneously. One is used for training data and the other is used for testing data. The accuracy of the classification is improved from the previous works by adding outpost vectors generated from prior samples. The testing function is able to test data continuously without being interrupted while the training function is being executed. The framework is designed for a parallel processing and/or a distributedprocessing environment due to the highly demanded processing power of the repetitive training process of the neuralnetwork.
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