A Representation Theory for Morphological Image and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time series and dependent variables
Physica D
Minimal representations for translation-invariant set mappings by mathematical morphology
SIAM Journal on Applied Mathematics
Morphology neural networks: an introduction with applications
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
Applied Intelligence
Evolving neural networks through augmenting topologies
Evolutionary Computation
Nonlinear systems and neural networks with hybrid morphological/rank/linear nodes: optimal design and applications to image processing and pattern recognition
Neural Computing and Applications
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Finding the embedding dimension and variable dependencies in time series
Neural Computation
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
MRL-filters: a general class of nonlinear systems and their optimal design for image processing
IEEE Transactions on Image Processing
Combination of artificial neural-network forecasters for prediction of natural gas consumption
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Quarterly Time-Series Forecasting With Neural Networks
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
An evolutionary approach to design dilation-erosion perceptrons for stock market indices forecasting
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
Hybrid morphological methodology for software development cost estimation
Expert Systems with Applications: An International Journal
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
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This paper presents a hybrid intelligent methodology to design increasing translation invariant morphological operators applied to Brazilian stock market prediction (overcoming the random walk dilemma). The proposed Translation Invariant Morphological Robust Automatic phase-Adjustment (TIMRAA) method consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) with a Quantum-Inspired Evolutionary Algorithm (QIEA), which searches for the best time lags to reconstruct the phase space of the time series generator phenomenon and determines the initial (sub-optimal) parameters of the MMNN. Each individual of the QIEA population is further trained by the Back Propagation (BP) algorithm to improve the MMNN parameters supplied by the QIEA. Also, for each prediction model generated, it uses a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in stock market time series. Furthermore, an experimental analysis is conducted with the proposed method through four Brazilian stock market time series, and the achieved results are discussed and compared to results found with random walk models and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) and Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) methods.