179 research outputs found
Design for novel enhanced weightless neural network and multi-classifier.
Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems.
A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN.
Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems
Using a weightless neural network to forecast stock prices: A case study of Nigerian stock exchange
This research work, proposes forecasting stock prices in the stock market industry in Nigeria using a Weightless Neural Network (WNN). A neural network application used to demonstrate the application of the WNN in the forecasting of stock prices in the market is designed and implemented in Visual Foxpro 6.0. The proposed network is tested with stock data obtained from the Nigeria Stock Exchange. This
system is compared with Single Exponential Smoothing (SES) model. The WNN error value is found to be 0.39 while that of SES is 9.78, based on these values, forecasting with the WNN is observed to be more accurate and closer to the real data than those using the SES model
A New Classification Technique in Mobile Robot Navigation
This paper presents a novel pattern recognition algorithm that use weightless neural network (WNNs) technique.This technique plays a role of situation classifier to judge the situation around the mobile robot environment and makes control decision in mobile robot navigation. The WNNs technique is choosen due to significant advantages over conventional neural network, such as they can be easily implemented in hardware using standard RAM, faster in training phase and work with small resources. Using a simple classification algorithm, the similar data will be grouped with each other and it will be possible to attach similar data classes to specific local areas in the mobile robot environment. This strategy is demonstrated in simple mobile robot powered by low cost microcontrollers with 512 bytes of RAM and low cost sensors. Experimental result shows, when number of neuron increases the average environmental recognition ratehas risen from 87.6% to 98.5%.The WNNs technique allows the mobile robot to recognize many and different environmental patterns and avoid obstacles in real time. Moreover, by using proposed WNNstechnique mobile robot has successfully reached the goal in dynamic environment compare to fuzzy logic technique and logic function, capable of dealing with uncertainty in sensor reading, achieving good performance in performing control actions with 0.56% error rate in mobile robot speed
Advances in quantum machine learning
Here we discuss advances in the field of quantum machine learning. The
following document offers a hybrid discussion; both reviewing the field as it
is currently, and suggesting directions for further research. We include both
algorithms and experimental implementations in the discussion. The field's
outlook is generally positive, showing significant promise. However, we believe
there are appreciable hurdles to overcome before one can claim that it is a
primary application of quantum computation.Comment: 38 pages, 17 Figure
Theoretical results on a weightless neural classifier and application to computational linguistics
WiSARD Ă© um classificador n-upla, historicamente usado em tarefas de reconhecimento de padrĂ”es em imagens em preto e branco. Infelizmente, nĂŁo era comum que este fosse usado em outras tarefas, devido ĂĄ sua incapacidade de arcar com grandes volumes de dados por ser sensĂvel ao conteĂșdo aprendido. Recentemente, a tĂ©cnica de bleaching foi concebida como uma melhoria Ă arquitetura do classificador n-upla, como um meio de coibir a sensibilidade da WiSARD. Desde entĂŁo, houve um aumento na gama de aplicaçÔes construĂdas com este sistema de aprendizado. Pelo uso frequente de corpora bastante grandes, a etiquetação gramatical multilĂngue encaixa-se neste grupo de aplicaçÔes. Esta tese aprimora o mWANN-Tagger, um etiquetador gramatical sem peso proposto em 2012. Este texto mostra que a pesquisa em etiquetação multilĂngue com WiSARD foi intensificada atravĂ©s do uso de linguĂstica quantitativa e que uma configuração de parĂąmetros universal foi encontrada para o mWANN-Tagger. AnĂĄlises e experimentos com as bases da Universal Dependencies (UD) mostram que o mWANN-Tagger tem potencial para superar os etiquetadores do estado da arte dada uma melhor representação de palavra. Esta tese tambĂ©m almeja avaliar as vantagens do bleaching em relação ao modelo tradicional atravĂ©s do arcabouço teĂłrico da teoria VC. As dimensĂ”es VC destes foram calculadas, atestando-se que um classificador n-upla, seja WiSARD ou com bleaching, que possua N memĂłrias endereçadas por n-uplas binĂĄrias tem uma dimensĂŁo VC de exatamente N (2n â 1) + 1. Um paralelo foi entĂŁo estabelecido entre ambos os modelos, onde deduziu-se que a tĂ©cnica de bleaching Ă© uma melhoria ao mĂ©todo n-upla que nĂŁo causa prejuĂzos Ă sua capacidade de aprendizado.WiSARD Ă© um classificador n-upla, historicamente usado em tarefas de reconhecimento de padrĂ”es em imagens em preto e branco. Infelizmente, nĂŁo era comum que este fosse usado em outras tarefas, devido ĂĄ sua incapacidade de arcar com grandes volumes de dados por ser sensĂvel ao conteĂșdo aprendido. Recentemente, a tĂ©cnica de bleaching foi concebida como uma melhoria Ă arquitetura do classificador n-upla, como um meio de coibir a sensibilidade da WiSARD. Desde entĂŁo, houve um aumento na gama de aplicaçÔes construĂdas com este sistema de aprendizado. Pelo uso frequente de corpora bastante grandes, a etiquetação gramatical multilĂngue encaixa-se neste grupo de aplicaçÔes. Esta tese aprimora o mWANN-Tagger, um etiquetador gramatical sem peso proposto em 2012. Este texto mostra que a pesquisa em etiquetação multilĂngue com WiSARD foi intensificada atravĂ©s do uso de linguĂstica quantitativa e que uma configuração de parĂąmetros universal foi encontrada para o mWANN-Tagger. AnĂĄlises e experimentos com as bases da Universal Dependencies (UD) mostram que o mWANN-Tagger tem potencial para superar os etiquetadores do estado da arte dada uma melhor representação de palavra. Esta tese tambĂ©m almeja avaliar as vantagens do bleaching em relação ao modelo tradicional atravĂ©s do arcabouço teĂłrico da teoria VC. As dimensĂ”es VC destes foram calculadas, atestando-se que um classificador n-upla, seja WiSARD ou com bleaching, que possua N memĂłrias endereçadas por n-uplas binĂĄrias tem uma dimensĂŁo VC de exatamente N (2n â 1) + 1. Um paralelo foi entĂŁo estabelecido entre ambos os modelos, onde deduziu-se que a tĂ©cnica de bleaching Ă© uma melhoria ao mĂ©todo n-upla que nĂŁo causa prejuĂzos Ă sua capacidade de aprendizado
Rejection-oriented learning without complete class information
Machine Learning is commonly used to support decision-making in numerous, diverse contexts. Its usefulness in this regard is unquestionable: there are complex systems built on the top of machine learning techniques whose descriptive and predictive capabilities go far beyond those of human beings. However, these systems still have limitations, whose analysis enable to estimate their applicability and confidence in various cases. This is interesting considering that abstention from the provision of a response is preferable to make a mistake in doing so. In the context of classification-like tasks, the indication of such inconclusive output is called rejection. The research which culminated in this thesis led to the conception, implementation and evaluation of rejection-oriented learning systems for two distinct tasks: open set recognition and data stream clustering. These system were derived from WiSARD artificial neural network, which had rejection modelling incorporated into its functioning. This text details and discuss such realizations. It also presents experimental results which allow assess the scientific and practical importance of the proposed state-of-the-art methodology.Aprendizado de MĂĄquina Ă© comumente usado para apoiar a tomada de decisĂŁo em numerosos e diversos contextos. Sua utilidade neste sentido Ă© inquestionĂĄvel: existem sistemas complexos baseados em tĂ©cnicas de aprendizado de mĂĄquina cujas capacidades descritivas e preditivas vĂŁo muito alĂ©m das dos seres humanos. Contudo, esses sistemas ainda possuem limitaçÔes, cuja anĂĄlise permite estimar sua aplicabilidade e confiança em vĂĄrios casos. Isto Ă© interessante considerando que a abstenção da provisĂŁo de uma resposta Ă© preferĂvel a cometer um equĂvoco ao realizar tal ação. No contexto de classificação e tarefas similares, a indicação desse resultado inconclusivo Ă© chamada de rejeição. A pesquisa que culminou nesta tese proporcionou a concepção, implementação e avaliação de sistemas de aprendizado orientados `a rejeição para duas tarefas distintas: reconhecimento em cenĂĄrio abertos e agrupamento de dados em fluxo contĂnuo. Estes sistemas foram derivados da rede neural artificial WiSARD, que teve a modelagem de rejeição incorporada a seu funcionamento. Este texto detalha e discute tais realizaçÔes. Ele tambĂ©m apresenta resultados experimentais que permitem avaliar a importĂąncia cientĂfica e prĂĄtica da metodologia de ponta proposta
ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks
The deployment of AI models on low-power, real-time edge devices requires
accelerators for which energy, latency, and area are all first-order concerns.
There are many approaches to enabling deep neural networks (DNNs) in this
domain, including pruning, quantization, compression, and binary neural
networks (BNNs), but with the emergence of the "extreme edge", there is now a
demand for even more efficient models. In order to meet the constraints of
ultra-low-energy devices, we propose ULEEN, a model architecture based on
weightless neural networks. Weightless neural networks (WNNs) are a class of
neural model which use table lookups, not arithmetic, to perform computation.
The elimination of energy-intensive arithmetic operations makes WNNs
theoretically well suited for edge inference; however, they have historically
suffered from poor accuracy and excessive memory usage. ULEEN incorporates
algorithmic improvements and a novel training strategy inspired by BNNs to make
significant strides in improving accuracy and reducing model size. We compare
FPGA and ASIC implementations of an inference accelerator for ULEEN against
edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we
demonstrate classification on the MNIST dataset at 14.3 million inferences per
second (13 million inferences/Joule) with 0.21 s latency and 96.2%
accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69
million inferences/Joule) with 0.31 s latency and 95.83% accuracy. In a
45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million
inferences/second at 98.46% accuracy, while a quantized Bit Fusion model
achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy.
In our search for ever more efficient edge devices, ULEEN shows that WNNs are
deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from
arXiv:2203.01479, most notably sections 5E and 5F.
The use of neural networks to characterise problematic arc sounds
Automation of electric arc welding has been at the centre of considerable debate and the
subject of much research for several decades. One conclusion drawn from all this effort is
that there seems to be no single system that can monitor all of the variables and subsequently,
fully control any welding process. To date there has been considerable success
in the development of seam tracking systems employing various sensing techniques,
good progress has been made in the area of penetration measurement and worthwhile
use has been made of the integration of expert systems and modelling software within
these control domains.
Skilled welders develop their own monitoring and control systems and it has been observed
that part of this expertise is the ability to listen subconsciously to the sound of the
arc and to alter the electrode position in response to an adverse change in arc noise.
Attempts have been made to analyse these sounds using both conventional techniques
and more recently expert systems, neither have delivered any usable information. This
paper describes a new approach involving the use of neural networks in the identification
of sounds which indicate that the welding system is drifting out of control
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