Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

FLANN Based Model to Predict Stock Price Movements of Stock Indices

Financial Forecasting or specifically Stock Market prediction is one of the hottest fields of research lately due to its commercial applications owing to the high stakes and the kinds of attractive benefits that it has to offer. Forecasting the price movements in stock markets has been a major challenge for common investors, businesses, brokers and speculators. As more and more money is being invested the investors get anxious of the future trends of the stock prices in the market. The primary area of concern is to determine the appropriate time to buy, hold or sell. In their quest to forecast, the investors assume that the future trends in the stock market are based at least in part on present and past events and data [1]. However financial time-series is one of the most ‘noisiest’ and ‘non-stationary’ signals present and hence very difficult to forecas

Utforsking av overgangen fra tradisjonell dataanalyse til metoder med maskin- og dyp læring

Data analysis methods based on machine- and deep learning approaches are continuously replacing traditional methods. Models based on deep learning (DL) are applicable to many problems and often have better prediction performance compared to traditional methods. One major difference between the traditional methods and machine learning (ML) approaches is the black box aspect often associated with ML and DL models. The use of ML and DL models offers many opportunities but also challenges. This thesis explores some of these opportunities and challenges of DL modelling with a focus on applications in spectroscopy. DL models are based on artificial neural networks (ANNs) and are known to automatically find complex relations in the data. In Paper I, this property is exploited by designing DL models to learn spectroscopic preprocessing based on classical preprocessing techniques. It is shown that the DL-based preprocessing has some merits with regard to prediction performance, but there is considerable extra effort required when training and tuning these DL models. The flexibility of ANN architecture designs is further studied in Paper II when a DL model for multiblock data analysis is proposed which can also quantify the importance of each data block. A drawback of the DL models is the lack of interpretability. To address this, a different modelling approach is taken in Paper III where the focus is to use DL models in such a way as to retain as much interpretability as possible. The paper presents the concept of non-linear error modelling, where the DL model is used to model the residuals of the linear model instead of the raw input data. The concept is essentially a shrinking of the black box aspect since the majority of the data modelling is done by a linear interpretable model. The final topic explored in this thesis is a more traditional modelling approach inspired by DL techniques. Data sometimes contain intrinsic subgroups which might be more accurately modelled separately than with a global model. Paper IV presents a modelling framework based on locally weighted models and fuzzy partitioning that automatically finds relevant clusters and combines the predictions of each local model. Compared to a DL model, the locally weighted modelling framework is more transparent. It is also shown how the framework can utilise DL techniques to be scaled to problems with huge amounts of data.Metoder basert på maskin- og dyp læring erstatter i stadig økende grad tradisjonell datamodellering. Modeller basert på dyp læring (DL) kan brukes på mange problemer og har ofte bedre prediksjonsevne sammenlignet med tradisjonelle metoder. En stor forskjell mellom tradisjonelle metoder og metoder basert på maskinlæring (ML) er den "svarte boksen" som ofte forbindes med ML- og DL-modeller. Bruken av ML- og DL-modeller åpner opp for mange muligheter, men også utfordringer. Denne avhandlingen utforsker noen av disse mulighetene og utfordringene med DL modeller, fokusert på anvendelser innen spektroskopi. DL-modeller er basert på kunstige nevrale nettverk (KNN) og er kjent for å kunne finne komplekse relasjoner i data. I Artikkel I blir denne egenskapen utnyttet ved å designe DL-modeller som kan lære spektroskopisk preprosessering basert på klassiske preprosesseringsteknikker. Det er vist at DL-basert preprosessering kan være gunstig med tanke på prediksjonsevne, men det kreves større innsats for å trene og justere disse DL-modellene. Fleksibiliteten til design av KNN-arkitekturer er studert videre i Artikkel II hvor en DL-modell for analyse av multiblokkdata er foreslått, som også kan kvantifisere viktigheten til hver datablokk. En ulempe med DL-modeller er manglende muligheter for tolkning. For å adressere dette, er en annen modelleringsframgangsmåte brukt i Artikkel III, hvor fokuset er på å bruke DL-modeller på en måte som bevarer mest mulig tolkbarhet. Artikkelen presenterer konseptet ikke-lineær feilmodellering, hvor en DL-modell blir bruk til å modellere residualer fra en lineær modell i stedet for rå inputdata. Konseptet kan ses på som en krymping av den svarte boksen, siden mesteparten av datamodelleringen er gjort av en lineær, tolkbar modell. Det siste temaet som er utforsket i denne avhandlingen er nærmere en tradisjonell modelleringsvariant, men som er inspirert av DL-teknikker. Data har av og til iboende undergrupper som kan bli mer nøyaktig modellert hver for seg enn med en global modell. Artikkel IV presenterer et modelleringsrammeverk basert på lokalt vektede modeller og "fuzzy" oppdeling, som automatisk finner relevante grupperinger ("clusters") og kombinerer prediksjonene fra hver lokale modell. Sammenlignet med en DL-modell, er det lokalt vektede modelleringsrammeverket mer transparent. Det er også vist hvordan rammeverket kan utnytte teknikker fra DL for å skalere opp til problemer med store mengder data

Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS

This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind farm. Wind speed prediction is critical for a wind energy conversion system since it may greatly influence the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. In this study, a wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction schemes including data error tolerance and ease in adaptability. The proposed SCU-based control would help to reduce the size of the energy storage system for minimizing wind power fluctuation taking the advantage of prediction scheme. The model for prediction using ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions

Vehicle level health assessment through integrated operational scalable prognostic reasoners

Today’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

A Two-stage Classification Method for High-dimensional Data and Point Clouds

High-dimensional data classification is a fundamental task in machine learning and imaging science. In this paper, we propose a two-stage multiphase semi-supervised classification method for classifying high-dimensional data and unstructured point clouds. To begin with, a fuzzy classification method such as the standard support vector machine is used to generate a warm initialization. We then apply a two-stage approach named SaT (smoothing and thresholding) to improve the classification. In the first stage, an unconstraint convex variational model is implemented to purify and smooth the initialization, followed by the second stage which is to project the smoothed partition obtained at stage one to a binary partition. These two stages can be repeated, with the latest result as a new initialization, to keep improving the classification quality. We show that the convex model of the smoothing stage has a unique solution and can be solved by a specifically designed primal-dual algorithm whose convergence is guaranteed. We test our method and compare it with the state-of-the-art methods on several benchmark data sets. The experimental results demonstrate clearly that our method is superior in both the classification accuracy and computation speed for high-dimensional data and point clouds.Comment: 21 pages, 4 figure