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Khanna, Neha, i Neha Khanna@mdbc gov au. "Investigation of phytoplankton dynamics using time-series analysis of biophysical parameters in Gippsland Lakes, South-eastern Australia". RMIT University. Civil, Environmental and Chemical Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080226.123435.
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There is a need for ecological modelling to help understand the dynamics in ecological systems, and thus aid management decisions to maintain or improve the quality of the ecological systems. This research focuses on non linear statistical modelling of observations from an estuarine system, Gippsland Lakes, on the south-eastern coast of Australia. Feed forward neural networks are used to model chlorophyll time series from a fixed monitoring station at Point King. The research proposes a systematic approach to modelling in ecology using feed forward neural networks, to ensure: (a) that results are reliable, (b) to improve the understanding of dynamics in the ecological system, and (c) to obtain a prediction, if possible. An objective filtering algorithm to enable modelling is presented. Sensitivity analysis techniques are compared to select the most appropriate technique for ecological models. The research generated a chronological profile of relationships between biophysical parameters and chlorophyll level for different seasons. A sensitivity analysis of the models was used to understand how the significance of the biophysical parameters changes as the time difference between the input and predicted value changes. The results show that filtering improves modelling without introducing any noticeable bias. Partial derivative method is found to be the most appropriate technique for sensitivity analysis of ecological feed forward neural networks models. Feed forward neural networks show potential for prediction when modelled on an appropriate time series. Feed forward neural networks also show capability to increase understanding of the ecological environment. In this research, it can be seen that vertical gradient and temperature are important for chlorophyll levels at Point King at time scales from a few hours to a few days. The importance of chlorophyll level at any time to chlorophyll levels in the future reduces as the time difference between them increases.
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Hadjiprocopis, Andreas. "Feed forward neural network entities". Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340374.
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Tanaka, Toshiyuki. "Control of growth dynamics of feed-forward neural network". Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13445.
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Al-Mudhaf, Ali F. "A feed forward neural network approach for matrix computations". Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/5010.
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A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to be construct. It should be noticed, however, that the present approach depends highly on the training algorithm of the FNN. Unfortunately, the available training methods; such as, the original Back-propagation (BP) algorithm, encounter many deficiencies when applied to matrix algebra problems; e. g., slow convergence due to improper choice of learning rates (LR). Thus, this study will focus on the development of new efficient and accurate FNN training methods. One improvement suggested to alleviate the problem of LR choice is the use of a line search with steepest descent method; namely, bracketing with golden section method. This provides an optimal LR as training progresses. Another improvement proposed in this study is the use of conjugate gradient (CG) methods to speed up the training process of the neural network. The computational feasibility of these methods is assessed on two matrix problems; namely, the LU-decomposition of both band and square ill-conditioned unsymmetric matrices and the inversion of square ill-conditioned unsymmetric matrices. In this study, two performance indexes have been considered; namely, learning speed and convergence accuracy. Extensive computer simulations have been carried out using the following training methods: steepest descent with line search (SDLS) method, conventional back propagation (BP) algorithm, and conjugate gradient (CG) methods; specifically, Fletcher Reeves conjugate gradient (CGFR) method and Polak Ribiere conjugate gradient (CGPR) method. The performance comparisons between these minimization methods have demonstrated that the CG training methods give better convergence accuracy and are by far the superior with respect to learning time; they offer speed-ups of anything between 3 and 4 over SDLS depending on the severity of the error goal chosen and the size of the problem. Furthermore, when using Powell's restart criteria with the CG methods, the problem of wrong convergence directions usually encountered in pure CG learning methods is alleviated. In general, CG methods with restarts have shown the best performance among all other methods in training the FNN for LU-decomposition and matrix inversion. Consequently, it is concluded that CG methods are good candidates for training FNN of matrix computations, in particular, Polak-Ribidre conjugate gradient method with Powell's restart criteria.
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Richards, Gareth D. "Implementation and capabilities of layered feed-forward networks". Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/11313.
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Mohammadi, Mohammad Mehdi. "PREDICTION OF WIND TURBINE BLADE FATIGUE LOADS USING FEED-FORWARD NEURAL NETWORKS". Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444115.
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In recent years, machine learning applications have gained great attention in the wind power industry. Among these, artificial neural networks have been utilized to predict the fatigue loads of wind turbine components such as rotor blades. However, the limited number of contributions and differences in the used databases give rise to several questions which this study has aimed to answer. Therefore, in this study, 5-min SCADA data from the Lillgrund wind farm has been used to train two feed-forward neural networks to predict the fatigue loads at the blade root in flapwise and edgewise directions in the shape of damage equivalent loads.The contribution of different features to the model’s performance is evaluated. In the absence of met mast measurements, mesoscale NEWA data are utilized to present the free flow condition. Also, the effect of wake condition on the model’s accuracy is examined. Besides, the generalization ability of the model trained on data points from one or multiple turbines on other turbines within the farm is investigated. The results show that the best accuracy was achieved for a model with 34 features, 5 hidden layers with 100 neurons in each hidden layer for the flapwise direction. For the edgewise direction, the best model has 54 features, 6 hidden layers, and 125 neurons in each hidden layer.For a model trained and tested on the same turbine, mean absolute percentage errors (MAPE) of 0.78% and 9.31% are achieved for the flapwise and edgewise directions, respectively. The seen difference is argued to be a result of not having enough data points throughout the range of edgewise moments. The use of NEWA data has been shown to improve the model’s accuracy by 10% for MAPE values, relatively. Training the model under different wake conditions did not improve the model showing that the wake effects are captured through the input features to some extent. Generalization of the model trained on data points from one turbine resulted in poor results in the flapwise direction. It was shown that using data points from multiple turbines can improve the model’s accuracy to predict loading on other turbines.
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Nyman, Jacob. "Machinery Health Indicator Construction using Multi-objective Genetic Algorithm Optimization of a Feed-forward Neural Network based on Distance". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298084.
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Assessment of machine health and prediction of future failures are critical for maintenance decisions. Many of the existing methods use unsupervised techniques to construct health indicators by measuring the disparity between the current state and either the healthy or the faulty states of the system. This approach can work well, but if the resulting health indicators are insufficient there is no easy way to steer the algorithm towards better ones. In this thesis a new method for health indicator construction is investigated that aims to solve this issue. It is based on measuring distance after transforming the sensor data into a new space using a feed-forward neural network. The feed-forward neural network is trained using a multi-objective optimization algorithm, NSGA-II, to optimize criteria that are desired in a health indicator. Thereafter the constructed health indicator is passed into a gated recurrent unit for remaining useful life prediction. The approach is compared to benchmarks on the NASA Turbofan Engine Degradation Simulation dataset and in regard to the size of the neural networks, the model performs relatively well, but does not outperform the results reported by a few of the more recent methods. The method is also investigated on a simulated dataset based on elevator weights with two independent failures. The method is able to construct a single health indicator with a desirable shape for both failures, although the latter estimates of time until failure are overestimated for the more rare failure type. On both datasets the health indicator construction method is compared with a baseline without transformation function and does in both cases outperform it in terms of the resulting remaining useful life prediction error using the gated recurrent unit. Overall, the method is shown to be flexible in generating health indicators with different characteristics and because of its properties it is adaptive to different remaining useful life prediction methods.Estimering av maskinhälsa och prognos av framtida fel är kritiska steg för underhållsbeslut. Många av de befintliga metoderna använder icke-väglett (unsupervised) lärande för att konstruera hälsoindikatorer som beskriver maskinens tillstånd över tid. Detta sker genom att mäta olikheter mellan det nuvarande tillståndet och antingen de friska eller fallerande tillstånden i systemet. Det här tillvägagångssättet kan fungera väl, men om de resulterande hälsoindikatorerna är otillräckliga så finns det inget enkelt sätt att styra algoritmen mot bättre. I det här examensarbetet undersöks en ny metod för konstruktion av hälsoindikatorer som försöker lösa det här problemet. Den är baserad på avståndsmätning efter att ha transformerat indatat till ett nytt vektorrum genom ett feed-forward neuralt nätverk. Nätverket är tränat genom en multi-objektiv optimeringsalgoritm, NSGA-II, för att optimera kriterier som är önskvärda hos en hälsoindikator. Därefter används den konstruerade hälsoindikatorn som indata till en gated recurrent unit (ett neuralt nätverk som hanterar sekventiell data) för att förutspå återstående livslängd hos systemet i fråga. Metoden jämförs med andra metoder på ett dataset från NASA som simulerar degradering hos turbofan-motorer. Med avseende på storleken på de använda neurala nätverken så är resultatet relativt bra, men överträffar inte resultaten rapporterade från några av de senaste metoderna. Metoden testas även på ett simulerat dataset baserat på elevatorer som fraktar säd med två oberoende fel. Metoden lyckas skapa en hälsoindikator som har en önskvärd form för båda felen. Dock så överskattar den senare modellen, som använde hälsoindikatorn, återstående livslängd vid estimering av det mer ovanliga felet. På båda dataseten jämförs metoden för hälsoindikatorkonstruktion med en basmetod utan transformering, d.v.s. avståndet mäts direkt från grund-datat. I båda fallen överträffar den föreslagna metoden basmetoden i termer av förutsägelsefel av återstående livslängd genom gated recurrent unit- nätverket. På det stora hela så visar sig metoden vara flexibel i skapandet av hälsoindikatorer med olika attribut och p.g.a. metodens egenskaper är den adaptiv för olika typer av metoder som förutspår återstående livslängd.
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Nigrini, L. B., i G. D. Jordaan. "Short term load forecasting using neural networks". Journal for New Generation Sciences, Vol 11, Issue 3: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/646.
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Published ArticleSeveral forecasting models are available for research in predicting the shape of electric load curves. The development of Artificial Intelligence (AI), especially Artificial Neural Networks (ANN), can be applied to model short term load forecasting. Because of their input-output mapping ability, ANN's are well-suited for load forecasting applications. ANN's have been used extensively as time series predictors; these can include feed-forward networks that make use of a sliding window over the input data sequence. Using a combination of a time series and a neural network prediction method, the past events of the load data can be explored and used to train a neural network to predict the next load point. In this study, an investigation into the use of ANN's for short term load forecasting for Bloemfontein, Free State has been conducted with the MATLAB Neural Network Toolbox where ANN capabilities in load forecasting, with the use of only load history as input values, are demonstrated.
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Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction". Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.
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Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictions calculated with stochastic methods such as generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA). By contrast the traditional approach was instead to use raw data as inputs. The proposed methods show superior result in yielding profit: at best 1.1% in the Swedish market and 4.6% in the American market. The neural network yielded more profit than the linear regression model, which is reasonable given its ability to find nonlinear patterns. The historical data was used with different window sizes. This gives a good understanding of the window size impact on the prediction performance.
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Gróf, Zoltán. "Realizace rozdělujících nadploch". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219781.
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The main aim of this master's thesis is to describe the subject of the implementation of decision boundaries with the help of artificial neural networks. The objective is to present theoretical knowledge concerning this field and on practical examples prove these statements. The work contains basic theoretical description of the field of pattern recognition and the field of feature based representation of objects. A classificator working on the basis of Bayes decision is presented in this part, and other types of classificators are named as well. The work then deals with artificial neural networks in more detail; it contains a theoretical description of their function and their abilities in the creation of decision boundaries in the feature plane. Examples are shown from literature for the use of neural networks in corresponding problems. As part of this work, the program ANN-DeBC was created using Matlab, for the generation of practical results about the usage of feed-forward neural networks for the implementation of decision boundaries. The work contains a detailed description of this program, and the achieved results are presented and analyzed. It is shown as well, how artificial neural networks are creating decision boundaries in the form of geometrical shapes. The effects of the chosen topology of the neural network and the number of training samples on the success of the classification are observed, and the minimal values of these parameters are determined for the successful creation of decision boundaries at the individual examples. Furthermore, it's presented how the neural networks behave at the classification of realistically distributed training samples, and what methods can affect the shape of the created decision boundaries.
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Prosperi, Maurizio. "Analysis of the EU MacSharry reform in Italian family farms : a multilayer feed-forward neural network model for evaluating economic and environmental impacts of the direct payments system". Kyoto University, 2005. http://hdl.handle.net/2433/144591.
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Kyoto University (京都大学)0048
新制・課程博士
博士(農学)
甲第11837号
農博第1527号
新制||農||919(附属図書館)
学位論文||H17||N4086(農学部図書室)
23597
UT51-2005-K503
京都大学大学院農学研究科生物資源経済学専攻
(主査)教授 加賀 爪優, 教授 吉田 昌之, 教授 小田 滋晃
学位規則第4条第1項該当
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Negrini, Melissa. "Tatto artificiale: studio ed implementazione di una rete neurale per la localizzazione di impatti". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17342/.
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Il tatto artificiale è un elaborato che tratta il sistema nervoso artificiale a partire dalla descrizione del sistema nervoso biologico. Nei primi capitoli viene descritto nel particolare il sistema nervoso biologico e di seguito il sistema nervoso artificiale mettendolo in relazione al primo e spiegando come vengono imitate le parti del sistema nervoso biologico da parti meccaniche e informatiche.
L’argomento centrale della tesi tratta la classificazione di impatti su una lastra piana a partire da algoritmi implementati in ambiente Matlab. Attraverso l’uso di 4 script si generano dei dati che vengono usati per addestrare la rete neurale che per mezzo di un algoritmo di feed-forward impara la relazione input/output.
I test sono stati attuati su una lastra di alluminio quadrata di lato 1m sulla quale sono stati applicati 4 sensori piezoelettrici in grado di restituire l’angolo dell’impatto. Lo studio è stato limitato agli angoli compresi nel perimetro delimitato dai sensori. In particolare, questa area è stata suddivisa in 25 classi geometricamente equivalenti. La rete ottenuta ha raggiunto l’obiettivo di stabilire in quale di quelle 25 aree è avvenuto l’impatto con un errore accettabile.
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Sasse, Jonathan Patrick. "Distinguishing Behavior from Highly Variable Neural Recordings Using Machine Learning". Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522755406249275.
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Gosal, Gurpreet Singh. "The use of Inverse Neural Networks in the Fast Design of Printed Lens Antennas". Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32249.
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In this thesis the major objective is the implementation of the inverse neural network concept in the design of printed lens (transmitarray) antenna. As it is computationally extensive to perform full-wave simulations for entire transmitarray structure and thereafter perform optimization, the idea is to generate a design database assuming that a unit cell of the transmitarray is situated inside a 2D infinite periodic structure. This way we generate a design database of transmission coefficient by varying the unit cell parameters. Since, for the actual design, we need dimensions for each cell on the transmitarray aperture and to do this we need to invert the design database.
The major contribution of this thesis is the proposal and the implementation of database inversion methodology namely inverse neural network modelling. We provide the algorithms for carrying out the inversion process as well as provide check results to demonstrate the reliability of the proposed methodology. Finally, we apply this approach to design a transmitarray antenna, and measure its performance.
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Přecechtěl, Roman. "Optimalizace řízení aktivního síťového prvku". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218166.
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The thesis deals with the use of neuronal networks for the control of telecommunication network elements. The aim of the thesis is to create a simulation model of network element with switching array with memory, in which the optimization kontrol switching array is solved by means of the neural network. All source code is created in integrated environment MATLAB. To training are used feed-forward backpropagation network. Miss achieve satisfactory result mistakes. Work apposite decision procedure given to problem and it is possible on ni tie up in an effort to find optimum solving.
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Vaško, Jan. "Využití prostředků umělé inteligence na kapitálových trzích". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222910.
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Diploma thesis deals with analyzing the possibility of using artificial intelligence, specifically artificial neural networks and fuzzy logic, on the capital markets as a tool to support decision making in business. The Matlab software is used for this purpose. The work is divided into three parts. The first part deals with theoretical knowledge, brief description of the current situationin is covered in a second part and the theoretical solutions are applied to the system in the third section.
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Kotol, Martin. "Neuronové modelování elektromegnetických polí uvnitř automobilů". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-390291.
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Disertační práce se věnuje využití umělých neuronových sítí pro modelování elektromagnetických polí uvnitř automobilů. První část práce je zaměřena na analytický popis šíření elektromagnetických vlny interiérem pomocí Nortonovy povrchové vlny. Následující část práce se věnuje praktickému měření a ověření analytických modelů. Praktická měření byla zdrojem trénovacích a verifikačních dat pro neuronové sítě. Práce se zaměřuje na kmitočtová pásma 3 až 11 GHz a 55 až 65 GHz.
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Smetana, Bedřich. "Algebraizace a parametrizace přechodových relací mezi strukturovanými objekty s aplikacemi v oblasti neuronových sítí". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-433543.
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The dissertation thesis investigates the modeling of the neural network activity with a focus on a multilayer forward neural network (MLP – Multi Layer Perceptron). In this often used structure of neural networks, time-varying neurons are used, along with an analogy in modeling hyperstructures of linear differential operators. Using a finite lemma and defined hyperoperation, a hyperstructure composed of neurons is defined for a given transient function. There are examined their properties with an emphasis on structures with a layout.
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Momeni, Mehdi. "Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy". Thesis, 2020. http://hdl.handle.net/1805/23569.
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Indiana University-Purdue University Indianapolis (IUPUI)Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.
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(9187742), SAYEDMOHAMMADMA VAEZ MOMENI. "FEED-FORWARD NEURAL NETWORK (FFNN) BASED OPTIMIZATION OF AIR HANDLING UNITS: A STATE-OF-THE-ART DATA-DRIVEN DEMAND-CONTROLLED VENTILATION STRATEGY". Thesis, 2020.
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Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.
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Bahrami, Asl Babak. "Futuristic Air Compressor System Design and Operation by Using Artificial Intelligence". Thesis, 2018. http://hdl.handle.net/1805/17932.
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Indiana University-Purdue University Indianapolis (IUPUI)The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in terms of energy consumption. Therefore, it becomes one of the primary targets when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.
2019-12-05
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(5931020), Babak Bahrami Asl. "FUTURISTIC AIR COMPRESSOR SYSTEM DESIGN AND OPERATION BY USING ARTIFICIAL INTELLIGENCE". Thesis, 2020.
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The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility.
System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.
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Huang, Hau-Luen, i 黃晧倫. "Independent Component Analysis-Based Feed-Forward Neural Network for Applications of Active Noise Control". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/73680378043527749812.
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碩士中興大學
機械工程學系所
95
In this study, an application of active noise control (ANC) using an independent component analysis-based feed-forward neural network (FFNN) is investigated. We consider a speech source and a noise source generating mixture signals in the space. Two microphones located at two distinct places are used to measure the mixture signals. Since the signal sources and the transmission paths are unknown, we apply a FFNN with MJH algorithm（FFNN_MJH）for the measured signals to obtain estimates of the signal sources. These estimates are then used for an ANC controller such that suitable control signal can be generated to drive a loudspeaker. It is desired that one of the microphone can observe the speech while ignoring the noise by use of the loudspeaker. Computer simulation shows that the observed microphone can effectively retain the speech while attenuating the noise with respective to tonal or harmonic noises. The proposed system also maintains a certain degree of robustness with respective to the uncertainty in the transmission paths of the loudspeaker, demonstrating its feasibility.
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林育宏, Yu-Hong Lin, i 林育宏. "Probability Distribution-Guided Water Flow-like Algorithm for Continuous Optimization: Application to Feed-forward Neural Network Training". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/b797w4.
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碩士國立臺灣大學
工業工程學研究所
103
Water flow-like algorithm (WFA) is inspired by the nature of water flow during circulating in the physical space. Initailly, WFA was developed to be a heuristic algorithm for combinatorial optimization. Thanks to WFA’s underlying ideal, this work propose a novel version of WFA for continuous optimization, called probability distribution-guided water flow-like algorithm (PWFA). In PWFA, basins are conceptualized as subspaces in the solution space, which help subflows to stochastically move toward the lowest position (the global optimum). To imitate the behavior of water flow heuristically, the flows perform spltting and moving, merging and precipitation operation to traverse in the space. Moreover, for evaluating PWFA’s performance, a large set of benchmark test functions and other basic optimization techiques from the literature are adopted for numerical test. In addition, the application to the training of feed-forward neural network (FNN) for pattern classification is also present as a test case for this algorithm. For the reason, a system prototype for solving continuous optimization problem and FNN parameter optimization is implemented by this work. The results show, first, that PWFA has a better performance than other optimization methods on uni-modal functions and multi-modal functions with single one optimum, and second, that this algorithm represents a competitive performance to other basic methods as solving multi-modal functions with many optimums. Additionally, the results of the application show PWFA is comparable to several optimization techniques included as well.
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CHOU, YUEH-CHING, i 周岳慶. "Combining Particle Swarm Optimization, Gravitational Search Algorithm and Fuzzy Rule to Improve the Classification Performance for Feed-forward Neural Network". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/28zh58.
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碩士國立勤益科技大學
工業工程與管理系
107
The Feed-forward Neural Network (FNN) is a kind of artificial neural network and has been widely used in medical diagnosis, data mining, securities market analysis and other fields. In FNN, during the learning process, the goal is to find the best combination of connection weights and biases in order to achieve the minimum error. However, in many cases, FNNs converge to local optimum but not global optimum. How to optimize the connection weights and deviations to achieve the minimum error is one of the purposes of this study. This study is to use the University of California Irvine (UCI) mechanical learning database of chronic kidney disease (CKD) and mesothelioma (MES) disease as the research object of this study. The research method firstly preprocesses the database and normalizes the data so that the data is between -1 and 1. Using FNN to learn the feature of each data, and using particle swarm optimization (PSO) and gravitational search algorithm (GSA) to optimize the weights and biases of FNN classifiers based on the algorithms inspired by observation of natural phenomena. In addition, referring to the FuzzyGSA proposed by González et al., the fuzzy rules is used to optimize the parameters of the GSA algorithm to improve the performance of the algorithm in the classifier. PSOGSA, proposed by Mirjalili et al., combines the social thinking ability (Gbest) in PSO with the local search ability of GSA. In this study, fuzzy rules are used to optimize the parameters of the PSOGSA algorithm, and FuzzyPSOGSA is proposed to accelerate the convergence of the algorithm in the later stage. In the part of the results, the learning errors generated by each algorithm in optimizing the classifier are discussed, and the proposed method is evaluated by using the confusion matrix. Our proposed method can provide doctors with a more accurate support decision-making system in the diagnosis of patients with CKD and MES cutaneous disease, to help doctors more accurately determine whether patients have diseases. In order to reduce the delay caused by mistakes in medical diagnosis and medical resources, doctors can reduce the waste of medical treatment and medical resources.
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Gan, Luyun. "Multi-label classification with optimal thresholding for multi-composition spectroscopic analysis". Thesis, 2019. http://hdl.handle.net/1828/11095.
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Spectroscopic analysis has several applications in physics, chemistry, bioinformatics, geophysics, astronomy, etc. It has been widely used for detecting mineral samples, gas emission, and food volatiles. Machine learning algorithms for spectroscopic analysis focus on either regression or single-label classification problems. Using multi-label classification to identify multiple chemical components from the spectrum, has not been explored. In this thesis, we implement Feed-forward Neural Network with Optimal Thresholding (FNN-OT) identifying gas species among a multi gas mixture in a cluttered environment. Spectrum signals are initially processed by a feed-forward neural network (FNN) model, which produces individual prediction scores for each gas. These scores will be the input of a following optimal thresholding (OT) system. Predictions of each gas component in one testing sample will be made by comparing its output score from FNN against a threshold from the OT system. If its output score is larger than the threshold, the prediction is 1 and 0 otherwise, representing the existence/non-existence of that gas component in the spectrum.
Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms FNN itself and conventional binary relevance - Partial Least Squares with Binary Relevance (PLS-BR). All three models are trained and tested on 18 synthesized datasets with 6 levels of \signal-to-noise ratio and 3 types of gas correlation. They are evaluated and compared with micro, macro and sample averaged precision, recall and F1 score. For mutually independent and randomly correlated gas data, FNN-OT yields better performance than FNN itself or the conventional PLS-BR, by significantly by increasing recall without sacrificing much precision. For positively correlated gas data, FNN-OT performs better in capturing information of positive label correlation from noisy datasets than the other two models.Graduate
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Oba, Pius Nwachukwu. "Quality analysis modelling for development of a process controller in resistance spot welding using neural networks techniques". Thesis, 2006. http://hdl.handle.net/10539/1676.
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Student Number : 9811923K -
PhD thesis -
School of Mechanical Engineering -
Faculty of Engineering and the Built EnvironmentMethods are presented for obtaining models used for predicting welded sample resistance and effective weld current (RMS) for desired weld diameter (weld quality) in the resistance spot welding process. These models were used to design predictive controllers for the welding process. A suitable process model forms an important step in the development and design of process controllers for achieving good weld quality with good reproducibility. Effective current, dynamic resistance and applied electrode force are identified as important input parameters necessary to predict the output weld diameter. These input parameters are used for the process model and design of a predictive controller. A three parameter empirical model with dependent and independent variables was used for curve fitting the nonlinear halfwave dynamic resistance. The estimates of the parameters were used to develop charts for determining overall resistance of samples for any desired weld diameter. Estimating resistance for samples welded in the machines from which dataset obtained were used to plot the chart yielded accurate results. However using these charts to estimate sample resistance for new and unknown machines yielded high estimation error. To improve the prediction accuracy the same set of data generated from the model were used to train four different neural network types. These were the Generalised Feed Forward (GFF) neural network, Multilayer Perceptron (MLP) network, Radial Basis Function (RBF) and Recurrent neural network (RNN). Of the four network types trained, the MLP had the least mean square error for training and cross validation of 0.00037 and 0.00039 respectively with linear correlation coefficient in testing of 0.999 and maximum estimation error range from 0.1% to 3%. A prediction accuracy of about 97% to 99.9%. This model was selected for the design and implementation of the controller for predicting overall sample resistance. Using this predicted overall sample resistance, and applied electrode force, a second model was developed for predicting required effective weld current for any desired weld diameter. The prediction accuracy of this model was in the range of 94% to 99%. The neural network predictive controller was designed using the MLP neural network models. The controller outputs effective current for any desired weld diameter and is observed to track the desired output accurately with same prediction accuracy of the model used which was about 94% to 99%. The controller works by utilizing the neural network output embedded in Microsoft Excel as a digital link library and is able to generate outputs for given inputs on activating the process by the push of a command button.
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Carvalho, João Gabriel Marques. "Electricity consumption forecast model for the DEEC based on machine learning tools". Master's thesis, 2020. http://hdl.handle.net/10316/90148.
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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e TecnologiaNesta tese apresentaremos o trabalho sobre a criação de uma rede neuronal de aprendizagem automática, capaz de realizar previsões energéticas. Com o aumento do consumo energético, devem desenvolvidas ferramentas capazes de prever o consumo. Esta necessidade levou à pesquisa deste tema.Procura-se explicar a história da aprendizagem automática, o que é a aprendizagem automática e como é que esta funciona. Também se procura explicar os seus antecedentes matemáticos, a utilização de redes neuronais e que ferramentas foram atualmente desenvolvidas; de forma a criar soluções de aprendizagem automática. A aprendizagem automática consiste num programa informático, que após treino é capaz de desempenhar tarefas de forma similar à mente humana. A rede neuronal (ANN) é uma das mais importantes ferramentas de aprendizagem automática, através da qual se pode obter informação fundamental.Para prever o consumo de energia no Departamento de Engenharia Eletrotécnica e de Computadores (DEEC) da Universidade de Coimbra, uma rede neural foi treinada usando dados reais do consumo total das torres do DEEC.Phyton foi a linguagem utilizada e recorreu-se ao logaritmo de regressão de aprendizagem supervisionada. Com esta previsão, comparam-se os dados obtidos com os dados reais, o que permite a sua análise. Os dados usados no treino da rede neuronal vão de 2015/julho/10 a 2017/dezembro/31, num total de 906 dias. Por cada dia do ano existe um máximo de 3 valores, considerando-se assim uma amostra pequena.A comparação final entre os dados reais e os dados previstos foi somente realizada no mês de janeiro de 2018. A partir dos dados obtidos realizaram-se previsões, apesar de um certo nível de discrepância; justificada pela pequena quantidade de dados disponíveis. No futuro, deve-se aumentar os dados de treino de forma a obter um maior número de variáveis de entrada. O principal objetivo proposto nesta tese foi atingido com sucesso. Com toda a pesquisa apresentada, buscou-se criar informação que permitisse ser um marco na criação de melhores soluções. Este é um campo extraordinário que no futuro permitirá elevar os nossos conhecimentos a outros níveis.
In this thesis, the design of a machine learning neural network capable of making energy predictions is presented. With the increase in energy consumption, tools for the prediction of energy consumption are gaining great importance and their implementation is required. This concern is the main goal of the presented work.We strive to explain the history of machine learning, what machine learning is and how it works. It is also sought to explain the mathematical background and use of neural networks and what tools have been developed nowadays to create machine learning solutions. Machine learning is a computer program that can perform trained tasks in a similar way as the human mind. The neural network (ANN) is one of the most used and important machine learning solution through which pivotal data can be obtained. For predicting the energy consumption at the Department of Electrical and Computer Engineering (DEEC) of the University of Coimbra, a neural network was trained using real data from the overall consumption of the DEEC towers.Phyton was the language used and the supervised learning regression algorithm utilized. With this prediction, we finally compare our data with real data, so that we may analyze it. The data used in the training of the neural network goes from 2015/July/10 to 2017/December/31, a total of 906 days. For each day of the year, there is a maximum of 3 values, which is considered a small sample, but the only one available The final comparison between real and predicted data was only done for the month of January 2018. From the data achieved, predictions were made, but with a certain level of discrepancy, that is explained with the low amount of data available. In the future, one of the things that should be considered is to enlarge the training datasets, considering a larger amount of input variables. The main goal proposed for this thesis was successfully obtained. With all the presented research it was strived to create text that would allow being a steppingstone in the creation of better solutions. This is an extraordinary field that in the future will be able to elevate our knowledge to a completely different level.