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Financial time series data is very chaotic, noisy, fluctuating and nonlinear as different events have occurred in various time periods. Therefore, it is very challenging for researchers to develop the accurate predictive model. Prediction for Foreign Exchange rate is also a very crucial task for N days ahead prediction because of volatile nature of Foreign Exchange rate data. It is also become highly desirable due to it's role in financial and managerial decision making capacity of any country. A lot of efforts have been done by researchers over many years for the development of efficient models to improve the forecasting accuracy. As a result, various important time series forecasting models have been evolved in literature. From the literature survey we have analyzed that statistical techniques are not able to efficiently predict the Foreign Exchange rate. Hence, different machine learning techniques have been used by many researchers for accurate prediction. Over the years different types of neural network models such as multi – layer perceptron, radial basis function neural network, functional link artificial neural network and integrated model such as auto – regressive integrated moving average models are developed to predict the currency exchange rates of different countries with varying parameters. In this paper, we divide our effort into two parts. In the first part, we have reviewed a few selected models based on neural networks and statistical methods including fundamental and technical aspects of currency exchange rate prediction. In the second part, we have made a thorough and careful empirical study of the models reviewed in part one. Our study reveals that the daily currency exchange rates with multi – layer neural network having Bayesian learning technique produces more accurate results against the multi – layer neural network with back propagation learning technique. Similarly, integrated models of radial basis function neural network and functional link neural networks produce less amount of error in comparison to single radial basis neural networks and functional link neural network models. Additionally, we critically analyze an integrated work on statistical model such as auto-regressive integrated moving average model with neural networks and revealed that the integrated models produces better results than the individual models. [ABSTRACT FROM AUTHOR]

This paper discusses an algorithm that provides a way to obtain ensembles of collaborating artificial neural networks (ANNs) online. That is, its purpose is to find solutions to problems based on the interaction of sets of, in principle, heterogeneous ANNs whose joint behaviour results in an emergent solution. This approach is intrinsically able to handle lifelong adaptation within the society in order to comply with changing situations or demands in dynamic environments. It is called Asynchronous Situated Coevolution (ASiCo) and was designed for the lifelong coevolution of artificial neural network societies. ASiCo deals with the evolutionary part of neuroevolution and it can support any type of neural network structure or even neural network construction mechanism. Consequently, it can be extended with some of the techniques found in single ANN neuroevolutionary mechanisms when considering the simultaneous evolution of network weights and network topology. The operation and characteristics of this strategy are illustrated through some experiments carried out using a well known benchmark collaboration task. [ABSTRACT FROM AUTHOR]

In this paper, a recently proposed evolutionary self-organizing map is extended and applied to visual tracking of objects in video sequences. The proposed approach uses a simple geometric template to track an object executing a smooth movement represented by affine transformations. The template is selected manually in the first frame and consists of a small number of keypoints and the neighborhood relations among them. The coordinates of the keypoints are used as the coordinates of the nodes of a non-regular grid defining a self-organizing map that represents the object. The weight vectors of each node in the output grid are updated by an evolutionary algorithm and used to locate the object frame by frame. Qualitative and quantitative evaluations indicate that the proposed approach present better results than those obtained by a direct method approach. Additionally, the proposed approach is evaluated under situations of partial occlusion and self-occlusion, and outliers, also presenting good results. [ABSTRACT FROM AUTHOR]

In this paper, an Evolutionary Artificial Neural Network (EANN) that combines the Fuzzy ARTMAP (FAM) network and a Hybrid Evolutionary Programming (HEP) model is introduced. The proposed FAM-HEP model, which combines the strengths of FAM and HEP, is able to construct its network structure autonomously as well as to perform learning and evolutionary search and adaptation concurrently. The effectiveness of the proposed FAM-HEP network is assessed empirically using several benchmark data sets and a real medical diagnosis problem. The performance of FAM-HEP is analyzed, and the results are compared with those of FAM-EP, FAM, and other classification models. In general, the results of FAM-HEP are better than those of FAM-EP and FAM, and are comparable with those from other classification models. The study also reveals the potential of FAM-HEP as an innovative EANN model for undertaking pattern classification problems in general, and a promising computerized decision support tool for tackling medical diagnosis tasks in particular. [ABSTRACT FROM AUTHOR]

This goal of the paper is introduction and experimental evaluation of neuro-genetic system for short-term stock index prediction. The system works according to the following scheme: first, a pool of input variables are defined through technical data analysis. Then GA is applied to find an optimal set of input variables for a one day prediction. Due to the high volatility of mutual relations between input variables, a particular set of inputs found by the GA is valid only for a short period of time and a new set of inputs is calculated every 5 trading days. The data is gathered from the German Stock Exchange (being the target market) and two other markets (Tokyo Stock Exchange and New York Stock Exchange) together with EUR/USD and USD/JPY exchange rates. The method of selecting input variables works efficiently. Variables which are no longer useful are exchanged with the new ones. On the other hand some, particularly useful, variables are consequently utilized by the GA in subsequent independent prediction periods. The proposed system works well in cases of both upward or downward trends. The effectiveness of the system is compared with the results of four other models of stock market trading. [ABSTRACT FROM AUTHOR]

A 2D triangle mesh simplification model is described in this paper, with the main objective of preserving the shape of the original mesh. The proposed model consists of a self-organizing algorithm whose objective is to generate the positions of the nodes of the simplified mesh; afterwards, a triangulation algorithm is performed to reconstruct the triangles of the new simplified mesh. The self-organizing algorithm is an unsupervised learning algorithm that provides a set of nodes representing the best approximation of the original mesh. An adaptation of the neural network algorithm is proposed with the primary objective to work in the context of urban transport networks. We verify the effectiveness of this model through the design and development of some urban network problems. Specifically, the algorithm is applied to two real problems, the first one is the design of a tramway network in a town, and the second one is the design of an information point network within a real bus transport network. [ABSTRACT FROM AUTHOR]