Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Neural Classifier Systems for Histopathologic Diagnosis

Neural network and statistical classification methods were applied to derive an objective grading for moderately and poorly differentiated lesions, based on characteristics of the nuclear placement patterns. Using a multilayer network after abbreviated training as a feature extractor followed by a quadratic Bayesian classifier allowed grade assignment agreeing with visual diagnostic consensus in 96% of fields from the training set of 500 fields, and a 77% of 130 fields of a test set

Inverse Kinematics Based on Fuzzy Logic and Neural Networks for the WAM-Titan II Teleoperation System

The inverse kinematic problem is crucial for robotics. In this paper, a solution algorithm is presented using artificial intelligence to improve the pseudo-inverse Jacobian calculation for the 7-DOF Whole Arm Manipulator (WAM) and 6-DOF Titan II teleoperation system. An investigation of the inverse kinematics based on fuzzy logic and artificial neural networks for the teleoperation system was undertaken. Various methods such as Adaptive Neural-Fuzzy Inference System (ANFIS), Genetic Algorithms (GA), Multilayer Perceptrons (MLP) Feedforward Networks, Radial Basis Function Networks (RBF) and Generalized Regression Neural Networks (GRNN) were tested and simulated using MATLAB. Each method for identification of the pseudo-inverse problem was tested, and the best method was selected from the simulation results and the error analysis. From the results, the Multilayer Perceptrons with Levenberg-Marquardt (MLP-LM) method had the smallest error and the fastest computation among the other methods. For the WAM-Titan II teleoperation system, the new inverse kinematics calculations for the Titan II were simulated and analyzed using MATLAB. Finally, extensive C code for the alternative algorithm was developed, and the inverse kinematics based on the artificial neural network with LM method is implemented in the real system. The maximum error of Cartesian position was 1.3 inches, and from several trajectories, 75 % of time implementation was achieved compared to the conventional method. Because fast performance of a real time system in the teleoperation is vital, these results show that the new inverse kinematics method based on the MLP-LM is very successful with the acceptable error

Algumas aplicações da Inteligência Artificial em Biotecnologia

The present work is a revision about neural networks. Initially presents a little introduction to neural networks, fuzzy logic, a brief history, and the applications of Neural Networks on Biotechnology. The chosen sub-areas of the applications of Neural Networks on Biotechnology are, Solid-State Fermentation Optimization, DNA Sequencing, Molecular Sequencing Analysis, Quantitative Structure-Activity Relationship, Soft Sensing, Spectra Interpretation, Data Mining, each one use a special kind of neural network like feedforward, recurrent, siamese, art, among others. Applications of the Neural-Networks in spectra interpretation and Quantitative Structure-activity relationships, is a direct application to Chemistry and consequently also to Biochemistry and Biotechnology. Soft Sensing is a special example for applications on Biotechnology. It is a method to measure variables that normally can’t be directly measure. Solid state fermentation was optimized and presenting, as result, a strong increasing of production efficiency.O presente trabalho é uma revisão sobre redes neurais. Inicialmente apresenta uma breve introdução a redes neurais, lógica difusa, um breve histórico, e aplicações de Redes Neurais em Biotecnologia. As subáreas escolhidas para aplicação das redes neurais são, Otimização da Fermentação no Estado-Sólido, Sequenciamento de DNA, Análise Molecular Sequencial, Relação Quantitativa Strutura-Atividade, Sensores inteligentes, Interpretação de espectros, Mineração de Dados, sendo que cada um usa um tipo especial de rede neural, tais como feed forward, recorrente, siamesa, art, entre outros. Aplicações de Redes Neurais em interpretação de espectros e Relação Quantitativa Estrutura-Atividade, como uma aplicação direta à química e consequentemente também para a Bioquímica e Biotecnologia. Os sensores Inteligentes são um exemplo especial de aplicação em Biotecnologia. É um método de medir variáveis que normalmente não podem ser medidas de forma direta. Fermentações no Estado-sólido foram otimizadas e, apresentaram como resultado um forte aumento do rendimento na produção final

Combined optimization algorithms applied to pattern classification

Accurate classification by minimizing the error on test samples is the main goal in pattern classification. Combinatorial optimization is a well-known method for solving minimization problems, however, only a few examples of classifiers axe described in the literature where combinatorial optimization is used in pattern classification. Recently, there has been a growing interest in combining classifiers and improving the consensus of results for a greater accuracy. In the light of the "No Ree Lunch Theorems", we analyse the combination of simulated annealing, a powerful combinatorial optimization method that produces high quality results, with the classical perceptron algorithm. This combination is called LSA machine. Our analysis aims at finding paradigms for problem-dependent parameter settings that ensure high classifica, tion results. Our computational experiments on a large number of benchmark problems lead to results that either outperform or axe at least competitive to results published in the literature. Apart from paxameter settings, our analysis focuses on a difficult problem in computation theory, namely the network complexity problem. The depth vs size problem of neural networks is one of the hardest problems in theoretical computing, with very little progress over the past decades. In order to investigate this problem, we introduce a new recursive learning method for training hidden layers in constant depth circuits. Our findings make contributions to a) the field of Machine Learning, as the proposed method is applicable in training feedforward neural networks, and to b) the field of circuit complexity by proposing an upper bound for the number of hidden units sufficient to achieve a high classification rate. One of the major findings of our research is that the size of the network can be bounded by the input size of the problem and an approximate upper bound of 8 + √2n/n threshold gates as being sufficient for a small error rate, where n := log/SL and SL is the training set

ECG QRS Enhancement Using Artificial Neural Network

Soft computing is a new approach to construct intelligent systems. The complex real world problems require intelligent systems that combine knowledge, techniques and methodologies from various sources. Neural networks recognize patterns and adapt themselves to cope with changing environments. Artificial neural network has potential applications in the field of ECG diagnosis measures. So noise reduced QRS complex of ECG signal is of utmost importance for automatic ECG interpretation and analysis. Noise is an unwanted energy, which interferes with the desired signal. Noise cancellation is mainly used as interference canceling in ECG, echo elimination on long distance telephone transmission lines and antenna side lobe interference canceling. In the study, the ECG signal is trained following various artificial neural network based algorithms to enhance the QRS complex by reducing noise for further analysis

Neural Network Adaptations to Hardware Implementations

In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and nonideal responses, and restraining computational complexity. Furthermore, a broad range of hardware-friendly learning rules is presented, which allow for simpler and more reliable hardware implementations. The relevance of these neural network adaptations to hardware is illustrated by their application in existing hardware implementations

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Determination of baseflow quantity by using unmanned aerial vehicle (UAV) and Google Earth

Baseflow is most important in low-flow hydrological features [1]. It is a function of a large number of variables that include factors such as topography, geology, soil, vegetation, and climate. In many catchments, base flow is an important component of streamflow and, therefore, base flow separations have been widely studied and have a long history in science. Baseflow separation methods can be divided into two main groups: non-tracer-based and tracer- based separation methods of hydrology. Besides, the base flow is determined by fitting a unit hydrograph model with information from the recession limbs of the hydrograph and extrapolating it backward