Dissertations / Theses on the topic 'Fixed neural network'
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Puttige, Vishwas Ramadas Engineering & Information Technology Australian Defence Force Academy UNSW. "Neural network based adaptive control for autonomous flight of fixed wing unmanned aerial vehicles." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43736.
Full textHao, Haiyan. "Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study Data." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/84942.
Full textMaster of Science
Rava, Eleonora Maria Elizabeth. "Bioaugmentation of coal gasification stripped gas liquor wastewater in a hybrid fixed-film bioreactor." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/62789.
Full textThesis (PhD)--University of Pretoria, 2017.
Chemical Engineering
PhD
Unrestricted
Коломієць, Ольга Вікторівна. "Телеграм-бот для класифікації зображень твердих побутових відходів." Магістерська робота, Хмельницький національний університет, 2020. http://elar.khnu.km.ua/jspui/handle/123456789/9374.
Full textSilva, Carlos Alberto de Albuquerque. "Contribui??o para o estudo do embarque de uma rede neural artificial em field programmable gate array (FPGA)." Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15340.
Full textThis study shows the implementation and the embedding of an Artificial Neural Network (ANN) in hardware, or in a programmable device, as a field programmable gate array (FPGA). This work allowed the exploration of different implementations, described in VHDL, of multilayer perceptrons ANN. Due to the parallelism inherent to ANNs, there are disadvantages in software implementations due to the sequential nature of the Von Neumann architectures. As an alternative to this problem, there is a hardware implementation that allows to exploit all the parallelism implicit in this model. Currently, there is an increase in use of FPGAs as a platform to implement neural networks in hardware, exploiting the high processing power, low cost, ease of programming and ability to reconfigure the circuit, allowing the network to adapt to different applications. Given this context, the aim is to develop arrays of neural networks in hardware, a flexible architecture, in which it is possible to add or remove neurons, and mainly, modify the network topology, in order to enable a modular network of fixed-point arithmetic in a FPGA. Five synthesis of VHDL descriptions were produced: two for the neuron with one or two entrances, and three different architectures of ANN. The descriptions of the used architectures became very modular, easily allowing the increase or decrease of the number of neurons. As a result, some complete neural networks were implemented in FPGA, in fixed-point arithmetic, with a high-capacity parallel processing
Este estudo consiste na implementa??o e no embarque de uma Rede Neural Artificial (RNA) em hardware, ou seja, em um dispositivo program?vel do tipo field programmable gate array (FPGA). O presente trabalho permitiu a explora??o de diferentes implementa??es, descritas em VHDL, de RNA do tipo perceptrons de m?ltiplas camadas. Por causa do paralelismo inerente ?s RNAs, ocorrem desvantagens nas implementa??es em software, devido ? natureza sequencial das arquiteturas de Von Neumann. Como alternativa a este problema, surge uma implementa??o em hardware que permite explorar todo o paralelismo impl?cito neste modelo. Atualmente, verifica-se um aumento no uso do FPGA como plataforma para implementar as Redes Neurais Artificiais em hardware, explorando o alto poder de processamento, o baixo custo, a facilidade de programa??o e capacidade de reconfigura??o do circuito, permitindo que a rede se adapte a diferentes aplica??es. Diante desse contexto, objetivou-se desenvolver arranjos de redes neurais em hardware, em uma arquitetura flex?vel, nas quais fosse poss?vel acrescentar ou retirar neur?nios e, principalmente, modificar a topologia da rede, de forma a viabilizar uma rede modular em aritm?tica de ponto fixo, em um FPGA. Produziram-se cinco s?nteses de descri??es em VHDL: duas para o neur?nio com uma e duas entradas, e tr?s para diferentes arquiteturas de RNA. As descri??es das arquiteturas utilizadas tornaram-se bastante modulares, possibilitando facilmente aumentar ou diminuir o n?mero de neur?nios. Em decorr?ncia disso, algumas redes neurais completas foram implementadas em FPGA, em aritm?tica de ponto fixo e com alta capacidade de processamento paralelo
Rek, Petr. "Knihovna pro návrh konvolučních neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385999.
Full textČermák, Justin. "Implementace umělé neuronové sítě do obvodu FPGA." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219363.
Full textKeller, Paul Edwin. "Fixed planar holographic interconnects for optically implemented neural networks." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185721.
Full textThai, Shee Meng. "Neural network modelling and control of coal fired boiler plant." Thesis, University of South Wales, 2005. https://pure.southwales.ac.uk/en/studentthesis/neural-network-modelling-and-control-of-coal-fired-boiler-plant(b5562ca0-e45e-44d8-aad2-ed2e3e114808).html.
Full textGaopande, Meghana Laxmidhar. "Exploring Accumulated Gradient-Based Quantization and Compression for Deep Neural Networks." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98617.
Full textMaster of Science
Neural networks are being employed in many different real-world applications. By learning the complex relationship between the input data and ground-truth output data during the training process, neural networks can predict outputs on new input data obtained in real time. To do so, a typical deep neural network often needs millions of numerical parameters, stored in memory. In this research, we explore techniques for reducing the storage requirements for neural network parameters. We propose software methods that convert 32-bit neural network parameters to values that can be stored using fewer bits. Our methods also convert a majority of numerical parameters to zero. Using special storage methods that only require storage of non-zero parameters, we gain significant compression benefits. On typical benchmarks like LeNet-300-100 (MNIST dataset), LeNet-5 (MNIST dataset), AlexNet (CIFAR-10 dataset) and VGG-16 (CIFAR-10 dataset), our methods can achieve up to 57.22x, 50.19x, 13.15x and 13.53x compression respectively. Storage benefits are achieved at the cost of classification accuracy, and we present our work in the light of the accuracy-compression trade-off.
Chong, Alex Zyh Siong. "The monitoring and control of stoker-fired boiler plant by neural networks." Thesis, University of South Wales, 1999. https://pure.southwales.ac.uk/en/studentthesis/the-monitoring-and-control-of-stokerfired-boiler-plant-by-neural-networks(edc913a9-3dc2-4159-ac89-9b04aef8465b).html.
Full textJoshi, Nishant. "Universality and Individuality in Recurrent Networks extended to Biologically inspired networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294208.
Full textAktiviteter i motorisk cortex visar sig vara dynamiska till sin natur. Att modellera dessa aktiviteter och jämföra dem med neurala inspelningar hjälper till att förstå den underliggande mekanismen för generering av dessa aktiviteter. För detta ändamål har återkommande neurala nätverk eller RNN uppstått som ett lämpligt verktyg. En tydlig förståelse för hur designvalen associerade med dessa nätverk påverkar den inlärda dynamiken och den interna representationen är fortfarande svårfångad. Ett tidigare arbete som utforskar de dynamiska egenskaperna hos diskreta RNN- arkitekturer (LSTM, UGRNN, GRU och Vanilla), såsom fastpunkts topologi och linjäriserad dynamik, förblir oförändrad när de tränas på 3-bitars Flip- Flop-uppgift. Däremot visar de att dessa nätverk har unik representationsgeometri. Målet för detta arbete är att förstå om dessa observationer också gäller för nätverk som är mer biologiskt realistiska när det gäller neural aktivitet. Därför valde vi att analysera hastighetsnätverk som har kontinuerlig dynamik och biologiskt realistiska anslutningsbegränsningar och på spikande neurala nätverk, där neuronerna kommunicerar via diskreta spikar som observerats i hjärnan. Vi reproducerar den ovannämnda studien för diskreta arkitekturer och visar sedan att fastpunkts topologi och linjäriserad dynamik förblir oförändrad för hastighetsnätverken men metoderna är otillräckliga för att hitta de fasta punkterna för spiknätverk. Representationsgeometrin för hastighetsnätverk och spiknätverk har visat sig skilja sig från de diskreta arkitekturerna men liknar varandra. Även om en liten delmängd av diskreta arkitekturer (LSTM) observeras vara nära i förhållande till hastighetsnäten. Vi visar att även om dessa olika nätverksarkitekturer med varierande grad av biologisk realism har individuella interna representationer, är den underliggande dynamiken under uppgiften universell. Vi observerar också att vissa diskreta nätverk har nära representationslikheter med hastighetsnätverk tillsammans med dynamiken. Följaktligen kan dessa diskreta nätverk vara bra kandidater för att reproducera och undersöka dynamiken i hastighetsnät.
Zeni, Lucas Maycon Hoff. "Determinação de regimes de escoamento gás-líquido em leito fixo utilizando redes neurais artificiais." Universidade Estadual do Oeste do Parana, 2012. http://tede.unioeste.br:8080/tede/handle/tede/1834.
Full textCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
Configuration of fixed bed that operates with biphasic flow is used in industrial operations such as the Fischer-Tropsch, hydrogenation, and residual water treatments. Vital information for the project and operation of this type of bed is in its characteristics fluid-dynamic and among these characteristics the flow regime because these have a direct influence transferring heat and mass present in the bed. In the two-phase flow with ascendant flow through fixed bed, three distinct regimes can be identified: the bubble regime, for low gas flow; pulsating regime, for moderate liquid and gas flow; and spray regime; for low flow of liquid and high flow rates of gas. Although there are different techniques to determine flow regimes, the most used is the visual identification. Thus, this research aims to develop, by using artificial neural networks (ANNs) a way to determine, for a given set of liquid-gas flow what out-flow regime the bed presents. To do so, firstly, the out-flow regime were identified by using water and air, respectively flux mass flowing varying from 2 to 16.5 kg.m-2.s-1 and from 0 to 0.6 kg.m-2.s-1, flowing up-words through a fixed bed packed with glass spheres measuring from 2.7 to 3.5 mm of diameter. The network proposed to identify the regimes contains Multiple Layers Perceptron architecture (PML) trained by the back propagation algorithm put together by applying the Multiple Back-Propagation (MBP) software, version 2.2.3 consistently with two input neurons, two intermediate layers, and four output neurons. The number of neurons of the intermediate layers was assorted to find out the best configuration. As activation of function, logistic, tangent, hyperbolic, and Gaussian were tested. Observed results showed that it is possible the identification of regimes through neural networks and among those tested the one that showed the best performance was the one that used the hyperbolic-tangent activation function; 10 neurons in the first hidden layer, and 12 neurons in the second hidden layer.
A configuração de leito fixo que opera com escoamento bifásico é muito utilizada em operações industriais, tais como síntese de Fischer-Tropsch, hidrogenação e tratamento de águas residuais. Uma informação vital para projeto e operação deste tipo de leito está nas características fluidodinâmicas, e dentre estas características podem ser citados os regimes de escoamento, pois estes influenciam diretamente nas transferências de calor e massa presentes no leito. No escoamento bifásico com fluxo ascendente através de leito fixo podem ser identificados três regimes distintos: regime bolha, para baixas vazões de gás; regime pulsante, para vazões moderadas de líquido e gás; e regime spray, para baixas vazões de líquidos e altas vazões de gás. Apesar de haver diferentes técnicas para a determinação dos regimes de escoamento, a mais empregada é a identificação visual. Sendo assim, esta pesquisa tem por objetivo desenvolver, por meio da utilização de redes neurais artificiais (RNA s), uma maneira de determinar, para um dado conjunto de vazões gás-líquido, qual regime de escoamento o leito apresenta. Para isto, os regimes de escoamento primeiramente foram identificados utilizando água e ar, respectivamente com fluxo mássico variando de 2 a 16,5 kg.m-2.s-1 e de 0 a 0,6 kg.m-2.s-1, escoando em fluxo ascendente por meio de um leito fixo recheado com esferas de vidro de diâmetro entre 2,7 e 3,5 mm. A rede proposta para a identificação dos regimes possui arquitetura perceptron de múltiplas camadas (MLP) treinada pelo algoritmo backpropagation e foi montada utilizando o programa freeware Multiple Back-Propagation (MBP) versão 2.2.3 sempre com dois neurônios de entrada, duas camadas intermediárias e quatro neurônios de saída. O número de neurônios das camadas intermediárias foi variado a fim de descobrir a melhor configuração. Como função de ativação, foram testadas as funções logística, tangente hiperbólica e gaussiana. Os resultados observados mostram que é possível a identificação dos regimes por meio de redes neurais e dentre as configurações testadas, a que apresentou melhor desempenho foi a rede que utilizou a função de ativação tangente hiperbólica, 10 neurônios na primeira camada oculta e 12 neurônios na segunda camada oculta.
Hamid, Muhammed Hamed. "Hyperspectral Image Generation, Processing and Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5905.
Full textD'arisbo, Thiago. "Modelagem híbrida do processo de troca iônica em colunas de leito fixo." Universidade Estadual do Oeste do Parana, 2011. http://tede.unioeste.br:8080/tede/handle/tede/1898.
Full textCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
Ion exchange is a process that is used in the treatment of aqueous industrial effluents containing organic compounds and heavy metals. The fixed bed columns are longer applied by allowing the process to occur continuously (cycles of regeneration). The design and process optimization of the ion exchange column requires the use of mathematical models. Phenomenological models of these systems involve the solution of partial differential and algebraic equations. The equilibrium data for ion exchange processes are usually described by the Mass Action Law (MAL), which can be considered non-ideality of aqueous and solid phases. Artificial Neural Networks (ANN) are being used successfully for the study of equilibrium data because they are empirical models and don t demand a mathematical rigor. This work aimed to evaluate the applicability of the hybrid model to describe the dynamics of ion exchange in fixed beds of binary systems. This system consists of partial differential equations obtained from mass balance in fluid phases in the ion exchanger and ANN to describe the balance. LAM was adjusted to experimental data of ion exchange equilibrium and then were generated 4200 data sets for each binary pair studied, which served as training for RNA. We tested networks with different structures, with one and two input layers. The 3-3-2 structure was used in the simulations of the hybrid model because it was the best represented the systems during the training phase. The differential equations were solved by the lines method. A computer program in FORTRAN language was developed for solving the model equations. DASSL subroutine was used to solve the equations. The performance of the hybrid model was evaluated from the results obtained with the phenomenological model, in which case the equilibrium description was made with the use of MAL. It also was the analysis of results from the comparison of experimental data. To evaluate the model we used data from the literature of ion exchange in Amberlite IR 120 resin on the systems Cu-Na and Zn-Na and in NaY zeolite on Fe-Na and Zn-Na. Both models were efficient to describe the dynamics of ion-exchange fixed bed columns, and the hybrid model had the advantage of the reduced computational time (82% reduction on average) as a result of not needing to solve a nonlinear equation.
A troca iônica é um processo muito utilizado no tratamento de efluentes industriais aquosos contendo compostos orgânicos e metais pesados. As colunas de leito fixo são mais aplicadas por permitir que o processo ocorra de maneira contínua (ciclos de regeneração). O projeto e a otimização de processos de troca iônica em coluna requer o uso de modelos matemáticos. Os modelos fenomenológicos destes sistemas envolvem a resolução de equações diferenciais parciais e algébricas. Os dados de equilíbrio de processos de troca iônica geralmente são descritos pela Lei da Ação das Massas (LAM), na qual podem ser consideradas as não idealidades das fases aquosa e sólida. As Redes Neurais Artificiais (RNA) estão sendo utilizadas com sucesso para o estudo destes dados de equilíbrio por serem modelos empíricos e não demandarem tal rigor matemático. Esta dissertação teve por objetivo avaliar a aplicabilidade do modelo híbrido para descrever a dinâmica do processo de troca iônica em leito fixo de sistemas binários. Este sistema é constituído de equações diferenciais parciais obtidas por meio de balanço de massa nas fases fluida e no trocador iônico e de RNA para descrever o equilíbrio. A LAM foi ajustada a dados experimentais de equilíbrio de troca iônica e, então, foram gerados conjuntos de 4200 dados para cada par binário estudado, os quais serviram como treinamento para a RNA. Foram testadas redes com diferentes estruturas, com uma e com duas camadas de entrada. A estrutura 3-3-2 foi utilizada nas simulações do modelo híbrido, pois foi a que melhor representou os sistemas na etapa de treinamento. As equações diferenciais foram resolvidas pelo método das linhas. Um programa computacional em linguagem FORTRAN foi desenvolvido para a resolução das equações do modelo. Foi utilizada a sub-rotina DASSL para resolver as equações. O desempenho do modelo híbrido foi avaliada a partir dos resultados obtidos com o modelo fenomenológico, sendo que neste caso a descrição do equilíbrio foi feita pelo uso da LAM. Também foi feita a análise dos resultados a partir da comparação dos dados experimentais. Para avaliar o modelo foram utilizados dados da literatura de troca iônica em resina Amberlite IR 120 dos sistemas Cu-Na e Zn-Na e na zeólita NaY dos sistemas Fe-Na e Zn-Na. Ambos os modelos foram eficientes para descrever a dinâmica de troca iônica de colunas de leito fixo, sendo que o modelo híbrido apresentou como vantagem o menor tempo computacional (82% de redução em média) em decorrência de não necessitar resolver a equação não-linear.
Hsiling, Chen Wung, and 陳勇雄. "Artificial Neural Network for the Diagnosis System of Service Recovery - Fixed Network Industry." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/39119937597486577617.
Full text國立臺北科技大學
生產系統工程與管理研究所
90
The characteristics of services include intangibility、inseparability、heterogeneity and perishability. Due to these characteristics, it is hard to make zero-defect services. Because service failure is unavoidable, service recovery can enhance customer satisfaction. Since 1998 the telecommunication market deregulation and liberalization, the market structure is different from before. The same target of every company is how to defeat the competitor and increase market shares. The most important thing is to understand the situation of service failure and then recovery it. This research discusses the kinds of service failure and recovery、the behavior of customer complain and the relation between service failure、service recovery、customer satisfaction and repurchase intention. We choice the research business is fixed network. By questionnaire, the input data is the variable of the kinds of service failure、its criticality and population. The output data is the transaction that customers hope to recovery. The model is established by statistical(Regression Analysis)and neural network methods(Back-Propagation Network、General Regression Neural Network). And then choice the smallest MSE model builds up the diagnosis system of service recovery. The research results showed that the slow speed of service occurs very often. The company recovery is the smallest cost and simple way to deal with customer complain. But customers that hope to recovery ways are fixed or improved the service failure、communicate rating discount. We choice General Regression Neural Network model and Visual Basic 6.0 to design the diagnosis system of service recovery. By this recovery system, company can do appropriate recovery strategy.
Peng, Shao-Wei, and 彭紹維. "Design Methodology for Hybrid Fixed Point/Binary Deep Neural Network for Low Power Object Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qryk24.
Full textLin, Jiun-Hong, and 林俊宏. "Power Load Signal Forecasting by Non-Fixed Fuzzy-Neural Networks." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/11704058403411560669.
Full text高雄工學院
電機電力研究所
85
Due to the very significance of power system load forecasting to electric util ity company, a wide variety of procedures for load forecasting have been propo sed in the last two decades. In recent years, the neural network (NN) technolo gy has been applied widely in this area based on its excellent learning abilit y. In most of these studies, NN structure utilized is fixed that means the NN keeps same size during the training and testing phases. It automatically devel ops an internal non-linear, complex relationship between power load and its in fluencing factors such as weather information through a training process on th e historical data. Then, the trained NN can be used to carry out the forecast no matter the training is appropriate or not. However, the correlations betwee n load and its influencing factors are various, depended very much on geograph ics, seasons, and the behavior of consumption of customers. the improper infor mation will make fixed NN to an ill-learning and cause a poor forecasting.In t his research, the NN structure utilized is non-fixed that means the NN's size keeps changing based on different situations during its learning and testing p rocesses. The correlations of load and its influencing factors on historical d ata are analyzed precisely. The problem of improper influencing is handled bef ore NN's training. Therefore, it makes non-fixed NN develop a more accurate mo del and then carry out a better forecasting. Furthermore, the phenomenon of ov ertraining is always happened in NN's learning with a non-stationary environme nt that makes an ill result of load forecasting . In this research, the modifi cation of learning rate based on fuzzy theory is also investigated. A proper p rocedure how to solve this training problem is proposed. From the point of com mercialization of NN, we hope to make NN technology utilizing in this field ha s a real potential and more promising.
LIN, YU-CHING, and 林玉青. "The Implementations for Fixed-Point and Floating-Point Recurrent Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9yf8k2.
Full text亞洲大學
光電與通訊學系
107
In this thesis, the research on the learning performances of fixed-point and float-ing-point implementations in single-layer and double-layer recurrent neural net-works is proposed. The recursive neural network is in fact the combination of feed-forward neural networks and infinite impulse response (IIR) filters. We have devel-oped the optimized filter structure and investigated its learning behaviors in finite-precision digital devices. In this paper, we test the robustness of fixed-point numbers and floating-point numbers with different finite precisions, and optimize the effect of finite precision on the state-space structure, so that the sensitivity of system pa-rameters with finite-precision can be effectively reduced in shorter word length. Once the optimal structure is synthesized, the RNN system can be stabilized with shorter word-length. Then, the performance of the finite precision of the single-layer and double-layer recurrent neural networks is compared. The results show that the fixed-point number before the optimization is slightly worse than the floating-point number. After optimization, the double-layer RNN has better learning performance than the single-layer RNN under finite precision. Finally, we verify its effectiveness by numerical examples.
Wei-ChungTseng and 曾微中. "Layer-wise Fixed Point Quantization for Deep Convolutional Neural Networks and Implementation of YOLOv3 Inference Engine." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/x46nq6.
Full text國立成功大學
電腦與通信工程研究所
107
With the increasing popularity of mobile devices and the effectiveness of deep learning-based algorithms, people try to put deep learning models on mobile devices. However, it is limited by the complexity of computational and software overhead. We propose an efficient framework for inference to fit resource-limited devices with about 1000 times smaller than Tensorflow in code size, and a layer-wised quantization scheme that allows inference computed by fixed-point arithmetic. The fixed-point quantization scheme is more efficient than floating point arithmetic with power consumption reduced to 8% left in cost grained evaluation and reduce model size to 40%~25% left, and keep TOP5 accuracy loss under 1% in Alexnet on ImageNet.
(8790188), Abhishek Navarkar. "MACHINE LEARNING MODEL FOR ESTIMATION OF SYSTEM PROPERTIES DURING CYCLING OF COAL-FIRED STEAM GENERATOR." Thesis, 2020.
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