Spelling suggestions: "subject:"multi layer perceptron"" "subject:"multi layer perceptrons""
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Rule extraction and knowledge transfer from radial basis function neural networksMcGarry, Kenneth J. January 2002 (has links)
No description available.
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A Deep Learning Approach to Predicting Diagnosis Code from Electronic Health Records / Djupinlärning för prediktion av diagnoskod utifrån elektroniska patientjournalerHåkansson, Ellinor January 2018 (has links)
Electronic Health Record (EHR) is an umbrella term encompassing demographics and health information of a patient from many different sources in a digital format. Deep learning has been used on EHRs in many successful studies and there is great potential in future implementations. In this study, diagnosis classification of EHRs with Multi-layer Perceptron models are studied. Two MLPs with different architectures are constructed and run on both a modified version of the EHR dataset and the raw data. A Random Forest is used as baseline for comparison. The MLPs are not successful in beating the baseline, with the best-performing MLP having a classification accuracy of 48.1%, which is 13.7 percentage points lower than that of the baseline. The results indicate that when the dataset is small, this approach should not be chosen. However, the dataset is growing over time and thus there is potential for continued research in the future. / Elektronisk patientjournal (EHR) är ett paraplybegrepp som används för att beskriva en digital samling av demografisk och medicinsk data från olika källor för en patient. Det finns stor potential i användandet av djupinlärning på dessa journaler och många framgångsrika studier har redan gjorts på området. I denna studie undersöks diagnosklassificering av elektroniska patientjournaler med Multi-layer perceptronmodeller. Två MLP-modeller av olika arkitekturer presenteras. Dessa körs på både en anpassad version av EHR-datamängden och på den råa EHR-datan. En Random Forest-modell används som baslinje för jämförelse. MLP-modellerna lyckas inte överträffa baslinjen, då den bästa MLP-modellen ger en klassifikationsnoggrannhet på 48,1%, vilket är 13,7 procentenheter mindre än baslinjens. Resultaten visar att en liten datamängd indikerar att djupinlärning bör väljas bort för denna typ av problem. Datamängden växer dock över tid, vilket gör områdetattraktivt för framtida studier.
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Developing basic soccer skills using reinforcement learning for the RoboCup small size leagueYoon, Moonyoung 03 1900 (has links)
Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: This study has started as part of a research project at Stellenbosch University
(SU) that aims at building a team of soccer-playing robots for the
RoboCup Small Size League (SSL). In the RoboCup SSL the Decision-
Making Module (DMM) plays an important role for it makes all decisions
for the robots in the team. This research focuses on the development of
some parts of the DMM for the team at SU.
A literature study showed that the DMM is typically developed in a
hierarchical structure where basic soccer skills form the fundamental building
blocks and high-level team behaviours are implemented using these basic
soccer skills. The literature study also revealed that strategies in the DMM
are usually developed using a hand-coded approach in the RoboCup SSL
domain, i.e., a specific and fixed strategy is coded, while in other leagues a
Machine Learning (ML) approach, Reinforcement Learning (RL) in particular,
is widely used. This led to the following research objective of this thesis,
namely to develop basic soccer skills using RL for the RoboCup Small Size
League. A second objective of this research is to develop a simulation environment
to facilitate the development of the DMM. A high-level simulator
was developed and validated as a result.
The temporal-difference value iteration algorithm with state-value functions
was used for RL, along with a Multi-Layer Perceptron (MLP) as a function
approximator. Two types of important soccer skills, namely shooting skills
and passing skills were developed using the RL and MLP combination. Nine
experiments were conducted to develop and evaluate these skills in various
playing situations. The results showed that the learning was very effective,
as the learning agent executed the shooting and passing tasks satisfactorily,
and further refinement is thus possible.
In conclusion, RL combined with MLP was successfully applied in this
research to develop two important basic soccer skills for robots in the
RoboCup SSL. These form a solid foundation for the development of a
complete DMM along with the simulation environment established in this
research. / AFRIKAANSE OPSOMMING: Hierdie studie het ontstaan as deel van 'n navorsingsprojek by Stellenbosch
Universiteit wat daarop gemik was om 'n span sokkerrobotte vir die
RoboCup Small Size League (SSL) te ontwikkel. Die besluitnemingsmodule
(BM) speel 'n belangrike rol in die RoboCup SSL, aangesien dit besluite vir
die robotte in die span maak. Hierdie navorsing fokus op ontwikkeling van
enkele komponente van die BM vir die span by SU.
'n Literatuurstudie het getoon dat die BM tipies ontwikkel word volgens
'n hiërargiese struktuur waarin basiese sokkervaardighede die fundamentele
boublokke vorm en hoëvlak spangedrag word dan gerealiseer deur hierdie
basiese vaardighede te gebruik. Die literatuur het ook getoon dat strategieë in die BM van die RoboCup SSL domein gewoonlik ontwikkel word deur
'n hand-gekodeerde benadering, dit wil s^e, 'n baie spesifieke en vaste strategie
word gekodeer, terwyl masjienleer (ML) en versterkingsleer (VL) wyd in
ander ligas gebruik word. Dit het gelei tot die navorsingsdoelwit in hierdie
tesis, naamlik om basiese sokkervaardighede vir robotte in die RoboCup SSL
te ontwikkel. 'n Tweede doelwit was om 'n simulasie-omgewing te ontwikkel
wat weer die ontwikkeling van die BM sou fasiliteer. Hierdie simulator is
suksesvol ontwikkel en gevalideer.
Die tydwaarde-verskil iterariewe algoritme met toestandwaarde-funksies is
gebruik vir VL saam met 'n multi-laag perseptron (MLP) vir funksiebenaderings.
Twee belangrike sokkervaardighede, naamlik doelskop- en aangeevaardighede
is met hierdie kombinasie van VL en MLP ontwikkel. Nege
eksperimente is uitgevoer om hierdie vaardighede in verskillende speelsituasies
te ontwikkel en te evalueer. Volgens die resultate was die leerproses baie
effektief, aangesien die leer-agent die doelskiet- en aangeetake bevredigend
uitgevoer het, en verdere verfyning is dus moontlik.
Die gevolgtrekking is dat VL gekombineer met MLP suksesvol toegepas is
in hierdie navorsingswerk om twee belangrike, basiese sokkervaardighede vir
robotte in die RoboCup SSL te ontwikkel. Dit vorm 'n sterk fondament vir
die ontwikkeling van 'n volledige BM tesame met die simulasie-omgewing
wat in hierdie werk daargestel is.
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Self-Organizing Error-Driven (Soed) Artificial Neural Network (Ann) for Smarter ClassificationJafari-Marandi, Ruholla 04 May 2018 (has links)
Classification tasks are an integral part of science, industry, medicine, and business; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this dissertation, motivated by learning styles in human brains, ANN’s shortcomings are assuaged and its learning power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. These benefits are in two directions: enhancing ANN’s learning power, and improving decision-making. First, the proposed method, named Self-Organizing Error-Driven (SOED) Artificial Neural Network (ANN), shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five famous benchmark datasets. Second, the hybridization creates space for inclusion of decision-making goals at the level of ANN’s learning. This gives the classifier the opportunity to handle the inconclusiveness of the data smarter and in the direction of decision-making goals. Through three case studies, naming 1) churn decision analytics, 2) breast cancer diagnosis, and 3) quality control decision making through thermal monitoring of additive manufacturing processes, this novel and cost-sensitive aspect of SOED has been explored and lead to much quantified improvement in decision-making.
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Comparative analysis of XGBoost, MLP and LSTM techniques for the problem of predicting fire brigade Iiterventions /Cerna Ñahuis, Selene Leya January 2019 (has links)
Orientador: Anna Diva Plasencia Lotufo / Abstract: Many environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and, as a result, they face an ever-increasing number of interventions, most of the time on constant resource. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters on constant resource is therefore to predict their workload, i.e., their number of interventions per hour, based on explanatory variables conditioning human activity. The present work aims to develop three models that are compared to determine if they can predict the firefighters' response load in a reasonable way. The tools chosen are the most representative from their respective categories in Machine Learning, such as XGBoost having as core a decision tree, a classic method such as Multi-Layer Perceptron and a more advanced algorithm like Long Short-Term Memory both with neurons as a base. The entire process is detailed, from data collection to obtaining the predictions. The results obtained prove a reasonable quality prediction that can be improved by data science techniques such as feature selection and adjustment of hyperparameters. / Resumo: Muitos fatores ambientais, econômicos e sociais estão levando as brigadas de incêndio a serem cada vez mais solicitadas e, como consequência, enfrentam um número cada vez maior de intervenções, na maioria das vezes com recursos constantes. Por outro lado, essas intervenções estão diretamente relacionadas à atividade humana, o que é previsível: os afogamentos em piscina ocorrem no verão, enquanto os acidentes de tráfego, devido a tempestades de gelo, ocorrem no inverno. Uma solução para melhorar a resposta dos bombeiros com recursos constantes é prever sua carga de trabalho, isto é, seu número de intervenções por hora, com base em variáveis explicativas que condicionam a atividade humana. O presente trabalho visa desenvolver três modelos que são comparados para determinar se eles podem prever a carga de respostas dos bombeiros de uma maneira razoável. As ferramentas escolhidas são as mais representativas de suas respectivas categorias em Machine Learning, como o XGBoost que tem como núcleo uma árvore de decisão, um método clássico como o Multi-Layer Perceptron e um algoritmo mais avançado como Long Short-Term Memory ambos com neurônios como base. Todo o processo é detalhado, desde a coleta de dados até a obtenção de previsões. Os resultados obtidos demonstram uma previsão de qualidade razoável que pode ser melhorada por técnicas de ciência de dados, como seleção de características e ajuste de hiperparâmetros. / Mestre
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Single-Channel Multiple Regression for In-Car Speech EnhancementITAKURA, Fumitada, TAKEDA, Kazuya, ITOU, Katsunobu, LI, Weifeng 01 March 2006 (has links)
No description available.
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: E-patarėjas galimybėms socialinės atskirties terpėje pasirinkti. Mašinos apsimokymo algoritmų pritaikymas / E-advisor for choosing possibilities within social isolation environment. Adaptation of Mashine Learning AlgorithmsSeselskis, Erikas 22 June 2006 (has links)
At the moment social exclusion is a topical problem in a whole Europe. That’s why innovative decisions are prompted for social exclusive group of people in order to facilitate their integration process into the labour market. The stepping-stone of this work is e-advisor for choosing possibilities within social isolation environment. This e-advisor is created in accordance with artificial neural network and considering to individual person’s features give suggestions for the most suitable professions. Also in this work is presented disease diagnostic model, which is defined by artificial neural network.
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Vision Based Obstacle Detection And Avoidance Using Low Level Image FeaturesSenlet, Turgay 01 April 2006 (has links) (PDF)
This study proposes a new method for obstacle detection and avoidance using low-level MPEG-7 visual descriptors. The method includes training a neural network with a subset of MPEG-7 visual descriptors extracted from outdoor scenes. The trained neural network is then used to estimate the obstacle presence in real outdoor videos and to perform obstacle avoidance. In our proposed method, obstacle avoidance solely depends on the estimated obstacle
presence data.
In this study, backpropagation algorithm on multi-layer perceptron neural network is utilized as a feature learning method. MPEG-7 visual descriptors are used to describe basic features of the given scene image and by further processing these features, input data for the neural network is obtained.
The learning/training phase is carried out on specially constructed synthetic video sequence with known obstacles. Validation and tests of the algorithms are performed on actual outdoor videos. Tests on indoor videos are also performed to evaluate the performance of the proposed algorithms in indoor scenes.
Throughout the study, OdBot 2 robot platform, which has been developed by the author, is used as reference platform.
For final testing of the obstacle detection and avoidance algorithms, simulation environment is used.
From the simulation results and tests performed on video sequences, it can be concluded that the proposed obstacle detection and avoidance methods are robust against visual changes in the environment that are common to most of the outdoor videos. Findings concerning the used methods are presented and discussed as an outcome of this study.
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Predicting Reactor Instability Using Neural NetworksHubert, Hilborn January 2022 (has links)
The study of the instabilities in boiling water reactors is of significant importance to the safety withwhich they can be operated, as they can cause damage to the reactor posing risks to both equipmentand personnel. The instabilities that concern this paper are progressive growths in the oscillatingpower of boiling-water reactors. As thermal power is oscillatory is important to be able to identifywhether or not the power amplitude is stable. The main focus of this paper has been the development of a neural network estimator of these insta-bilities, fitting a non-linear model function to data by estimating it’s parameters. In doing this, theambition was to optimize the networks to the point that it can deliver near ”best-guess” estimationsof the parameters which define these instabilities, evaluating the usefulness of these networks whenapplied to problems like this. The goal was to design both MLP(Multi-Layer Perceptron) and SVR/KRR(Support Vector Regres-sion/Kernel Rigde Regression) networks and improve them to the point that they provide reliableand useful information about the waves in question. This goal was accomplished only in part asthe SVR/KRR networks proved to have some difficulty in ascertaining the phase shift of the waves.Overall, however, these networks prove very useful in this kind of task, succeeding with a reasonabledegree of confidence to calculating the different parameters of the waves studied.
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Expanding multilayer perceptrons with a brain inspired activation algorithm : Experimental comparison of the performance of an activation enhanced multi layer perceptronWajud Abdul Aziz, Karar, Gripenberg, Kim Emil Leonard January 2022 (has links)
Machine learning is a field that is inspired by how humans and, by extension, the brain learns.The brain consists of a biological neural network that has neurons that are either active or inactive. Modern-day artificial intelligence is loosely based on how biological neural networks function. This paper investigates whether a multi layered perceptron that utilizes inactive/active neurons can reduce the number of active neurons during the forward and backward pass while maintaining accuracy. This is done by implementing a multi layer perceptron using a python environment and building a neuron activation algorithm on top of it. Results show that it ispossible to reduce the number of active neurons by around 30% with a negligible impact on test accuracy. Future works include algorithmic improvements and further testing if it is possible to reduce the total amount of mathematical operations in other neural network architectures with a bigger computational overhead.
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