Analysing rounding data using radial basis function neural networks model

Unspecified counting practices used in a data collection may create rounding to certain ‘based’ number that can have serious consequences on data quality. Statistical methods for analysing missing data are commonly used to deal with the issue but it could actually aggravate the problem. Rounded data are not missing data, instead some observations were just systematically lumped to certain based numbers reflecting the rounding process or counting behaviour. A new method to analyse rounded data would therefore be academically valuable. The neural network model developed in this study fills the gap and serves the purpose by complementing and enhancing the conventional statistical methods. The model detects, analyses, and quantifies the existence of periodic structures in a data set because of rounding. The robustness of the model is examined using simulated data sets containing specific rounding numbers of different levels. The model is also subjected to theoretical and numerical tests to confirm its validity before being used on real applications. Overall, the model performs very well making it suitable for many applications. The assessment results show the importance of using the right best fit in rounding detection. The detection power and cut-off point estimation also depend on data distribution and rounding based numbers. Detecting rounding of prime numbers is easier than non-prime numbers due to the unique characteristics of the former. The bigger the number, the easier is the detection. This is in a complete contrast with non-prime numbers, where the bigger the number, the more will be the “factor” numbers distracting rounding detection. Using uniform best fit on uniform data produces the best result and lowest cut-off point. The consequence of using a wrong best fit on uniform data is however also the worst. The model performs best on data containing 10-40% rounding levels as less or more rounding levels produce unclear rounding pattern or distort the rounding detection, respectively. The modulo-test method also suffers the same problem. Real data applications on religious census data confirms the modulo-test finding that the data contains rounding base 5, while applications on cigarettes smoked and alcohol consumed data show good detection results. The cigarettes data seem to contain rounding base 5, while alcohol consumption data indicate no rounding patterns that may be attributed to the ways the two data were collected. The modelling applications can be extended to other areas in which rounding is common and can have significant consequences. The modelling development can he refined to include data-smoothing process and to make it user friendly as an online modelling tool. This will maximize the model’s potential use

Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications

Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies

Transmission loss allocation using artificial neural networks

The introduction of deregulation and subsequent open access policy in electricity sector has brought competition in energy market. Allocation of transmission loss has become a contentious issue among the electricity producers and consumers. A closed form solution for transmission loss allocation does not exist due to the fact that transmission loss is a highly non-linear function of system states and it is a non-separable quantity. In absence of a closed form solution different utilities use different methods for transmission loss allocation. Most of these techniques involve complex mathematical operations and time consuming computations. A new transmission loss allocation tool based on artificial neural network has been developed and presented in this thesis. The proposed artificial neural network computes loss allocation much faster than other methods. A relatively short execution time of the proposed method makes it a suitable candidate for being a part of a real time decision making process. Most independent system variables can be used as inputs to this neural network which in turn makes the loss allocation procedure responsive to practical situations. Moreover, transmission line status (available or failed) was included in neural network inputs to make the proposed network capable of allocating loss even during the failure of a transmission line. The proposed neural networks were utilized to allocate losses in two types of energy transactions: bilateral contracts and power pool operation. Two loss allocation methods were utilized to develop training and testing patterns; the Incremental Load Flow Approach was utilized for loss allocation in the context of bilateral transaction and the Z-bus allocation was utilized in the context of pool operation. The IEEE 24-bus reliability network was utilized to conduct studies and illustrate numerical examples for bilateral transactions and the IEEE 14-bus network was utilized for pool operation. Techniques were developed to expedite the training of the neural networks and to improve the accuracy of results

Prediksi Indeks Harga Konsumen (IHK) Kelompok Perumahan, Air, Listrik, Gas, dan Bahan Bakar Menggunakan Metode Extreme Learning Machine

Indeks Harga Konsumen (IHK) merupakan salah satu indikator untuk mengukur tingkat inflasi di Indonesia. Pada tahun 2017 Inflasi yang terjadi di Indonesia menurut kelompok pengeluaran secara umum adalah sebesar 3,61%. Kelompok perumahan, air, listrik, gas, dan bahan bakar menjadi penyumbang inflasi terbesar sebanyak 5,14%. Maka dari itu prediksi perlu dilakukan untuk mengantisipasi serta mengurangi laju inflasi domestik. Prediksi yang dilakukan pada penelitian ini menggunakan metode Extreme Learning Machine (ELM) dengan inisialisasi bobot menggunakan Nguyen-Widrow. Tahapan metode ELM yang dilakukan yaitu proses normalisasi data, proses pelatihan untuk mendapatkan nilai bobot keluaran (output weight) yang nantinya akan digunakan pada proses pengujian untuk mendapatkan nilai keluaran output layer yang setelah di denormalisasi akan menjadi hasil prediksi dalam bentuk aktual. Selanjutnya adalah melakukan evaluasi hasil prediksi menggunakan Root Mean Squared Error (RMSE). Data yang digunakan dalam penelitian ini adalah 84 data Indeks Harga Konsumen kelompok perumahan perumahan, air, listrik, gas, dan bahan bakar dalam bentuk time-series periode Januari 2011 s.d. Desember 2017, diperoleh dari website resmi Bank Indonesia (www.bi.go.id). Hasil pengujian yang dilakukan pada penelitian ini adalah didapatkan nilai RMSE minimum sebesar 0,65 dengan jumlah fitur = 7, jumlah data latih 30 dan data uji 11, jumlah hidden neuron = 7, dan fungsi aktivasi yang cocok adalah fungsi sigmoid biner

Competitive co-evolution of trend reversal indicators using particle swarm optimisation

Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisation and neural networks could be used to discover competitive security trading agents that could enable the timing of buying and selling securities to maximise net profit and minimise risk over time. The investigated model attempts to identify security trend reversals with the help of technical analysis methodologies. Technical market indicators provide the necessary market data to the agents and reflect information such as supply, demand, momentum, volatility, trend, sentiment and retracement. All this is derived from the security price alone, which is one of the strengths of technical analysis and the reason for its use in this study. The model proposed in this thesis evolves trading strategies within a single pop- ulation of competing agents, where each agent is represented by a neural network. The population is governed by a competitive co-evolutionary particle swarm optimi- sation algorithm, with the objective of optimising the weights of the neural networks. A standard feed forward neural network architecture is used, which functions as a market trend reversal confidence. Ultimately, the neural network becomes an amal- gamation of the technical market indicators used as inputs, and hence is capable of detecting trend reversals. Timely trading actions are derived from the confidence output, by buying and short selling securities when the price is expected to rise or fall respectively. No expert trading knowledge is presented to the model, only the technical market indicator data. The co-evolutionary particle swarm optimisation model facilitates the discovery of favourable technical market indicator interpretations, starting with zero knowledge. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed hybrid computational intelligence model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. This indicates that the introduced model is effective in finding favourable strategies, based on observed historical security price data. Transaction costs were considered in the evaluation of the computational intelligent agents, making this a feasible model for a real-world application. CopyrightDissertation (MSc)--University of Pretoria, 2010.Computer Scienceunrestricte

Modular neural networks applied to pattern recognition tasks

Pattern recognition has become an accessible tool in developing advanced adaptive products. The need for such products is not diminishing but on the contrary, requirements for systems that are more and more aware of their environmental circumstances are constantly growing. Feed-forward neural networks are used to learn patterns in their training data without the need to discover by hand the relationships present in the data. However, the problem of estimating the required size of the neural network is still not solved. If we choose a neural network that is too small for a particular given task, the network is unable to "comprehend" the intricacies of the data. On the other hand if we choose a network size that is too big for the given task, we will observe that there are too many parameters to be tuned for the network, or we can fall in the "Curse of dimensionality" or even worse, the training algorithm can easily be trapped in local minima of the error surface. Therefore, we choose to investigate possible ways to find the 'Goldilocks' size for a feed-forward neural network (which is just right in some sense), being given a training set. Furthermore, we used a common paradigm used by the Roman Empire and employed on a wide scale in computer programming, which is the "Divide-et-Impera" approach, to divide a given dataset in multiple sub-datasets, solve the problem for each of the sub-dataset and fuse the results of all the sub-problems to form the result for the initial problem as a whole. To this effect we investigated modular neural networks and their performance

Resolution and assignment of defects in the hot-gas path of aircraft engines by means of the density distribution in the exhaust jet

Die Wartung und Instandhaltung ziviler Flugtriebwerke ist ein zeit- und kostenintensiver Prozess. Der Wartungsumfang kann häufig erst im laufenden Regenerationsprozess definiert werden, da ein Großteil der kostentreibenden Defekte im Heißgaspfad (HGP) erst nach der Demontage erkannt werden können. Die a priori Vorhersage der Wartungskosten und Durchlaufzeit unterliegt somit einer großen Unsicherheit, die im Widerspruch zum Wunsch der Fluglinien nach einem vorab definierten Festpreis für die Wartung steht. In dieser Arbeit wird ein Beitrag zu einer frühzeitigen Zustandsbeurteilung von Triebwerken vor der Demontage geleistet. Hierzu wird eine neuartige Methodik postuliert, die durch eine Kombination von numerischen Simulationen, optischen Messungen und den Einsatz von Mustererkennungsalgorithmen eine automatisierte Erkennung von Defekten im HGP von Triebwerken vor der Demontage ermöglicht. Die grundsätzlichen Voraussetzungen für eine solche Methodik werden auf ihre Umsetzbarkeit getestet. Die Methodik sieht vor, die Dichteverteilung im Abgasstrahl mit der tomographischen Background-Oriented Schlieren (BOS) Methode mehrdimensional zu rekonstruieren und so defektbasierte Dichtestrukturen aufzulösen, die ihren Ursprung in lokalen Beschädigungen im HGP haben. Es wird gezeigt, dass speziell algebraische Rekonstruktionsalgorithmen für die Rekonstruktion der Dichteverteilung im Abgasstrahl geeignet sind, da diese, verglichen mit den analytischen Algorithmen, eine höhere Rekonstruktionsqualität und somit Auflösung defektbasierter Dichtestrukturen ermöglichen. Durch eine neuartige Kombination beider Algorithmen kann die bisher übliche Problematik der Rekonstruktion großer Dichtegradienten beseitigt, und eine signifikante Verbesserung der Rekonstruktionsqualität erreicht werden. In einem Modellversuch an einer Ringbrennkammer wird anschließend gezeigt, dass eingebrachte Defekte einen Einfluss auf die Dichteverteilung im Abgasstrahl nehmen und hier mit der BOS-Methode detektiert werden können. Die Auflösung des implementierten Algorithmus ist hoch genug, um mit Hilfe geeigneter integraler Größen eine Parametrisierung der Defekteinflüsse auf die Dichteverteilung im Abgasstrahl zu erreichen. Die so gewählten integralen Parameter ermöglichen eine automatisierte Klassifizierung der BOS-Messungen mit Support-Vektor-Maschinen (SVM). Die Übertragung auf das Flugtriebwerk erfolgt anschließend mit numerischen Simulationen und synthetischen BOS-Messungen. Es wird gezeigt, dass die Kombination von BOS mit SVM geeignet ist, um Defekte und deren Kombinationen im HGP von Triebwerken automatisiert zu detektieren. Die zuvor am Modellversuch identifizierten integralen Parameter erlauben es, den Einfluss der untersuchten Defekte zu isolieren und so auch Defektkombinationen mit einem Mehr-Klassen SVM-Ansatz zu detektieren. Die Ergebnisse dieser Arbeit legen nah, dass der postulierte Ansatz eine signifikante Verbesserung in der frühzeitigen Zustandsbeurteilung von Triebwerken leisten kann

Smart grid framework analysis and artificial neutral network in load forecast

Power system is the one of the most critical parts of the whole energy utilization around the world. Recently people pay more attention to the energy utilization, new types of generations, storages and power utilization need to increase energy efficiency and reduce carbon emission. Due to the power grid currently is still mainly under the old-designed approach, it is increasingly exposed limitation on efficiency enhancement, security and reliability improvement, new technologies compatibility and meeting larger power capacity requirements. Thus, Smart Grid is 'born' to improve power grid for these requirements. It is an overlapping area between power system and digital technology, intelligent technology, communication technology and so on. Smart Grid can provide updates for nearly all sections of traditional power grid. It is a systematic framework that new technologies integration, system development strategy and planning, customers' awareness improvements and supports from all relevant areas. The areas must be operated in coordination and parallel. Firstly, this thesis introduces Smart Grid and Smart Metering on its definition, characteristics and deployment. Secondly, this thesis describes a load forecasting system for macro-grid. Artificial Neural Network (ANN) was introduced to achieve this work for its excellent mapping approximation ability. In the third section, thesis focuses on load forecasting for micro-grid. BackPropagation method is used to train the Multi-layer Perceptron (MLP) ANN and its results were compared to that from Radial Basis Function (RBF) ANN. Analysis was focused not only on the two networks but also ANN generalization problems and differences between micro-grid load and macro-grid load prediction