A Representation Theory for Morphological Image and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Why mathematical morphology needs complete lattices
Signal Processing
Time series and dependent variables
Physica D
Morphology neural networks: an introduction with applications
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
LADAR target detection using morphological shared-weighted neural networks
Machine Vision and Applications
Fuzzy lattice neurocomputing (FLN) models
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
Applied Intelligence
An Introduction to Morphological Neural Networks
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Journal of Mathematical Imaging and Vision
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study
Neural Processing Letters
Morphological/rank neural networks and their adaptive optimal design for image processing
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
Learning to Transform Time Series with a Few Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
A general framework for fuzzy morphological associative memories
Fuzzy Sets and Systems
Finding the embedding dimension and variable dependencies in time series
Neural Computation
Information Sciences: an International Journal
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
Towards the evaluation of time series protection methods
Information Sciences: an International Journal
On the application of Associative Morphological Memories to Hyperspectral Image Analysis
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Discriminative Learning for Dynamic State Prediction
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to morphological perceptrons with competitive learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A Hybrid Intelligent Morphological Approach for Stock Market Forecasting
Neural Processing Letters
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Implicative Fuzzy Associative Memories
IEEE Transactions on Fuzzy Systems
Morphological associative memories
IEEE Transactions on Neural Networks
Fuzzy lattice neural network (FLNN): a hybrid model for learning
IEEE Transactions on Neural Networks
Automatic target detection using entropy optimized shared-weight neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
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
Lattice algebra approach to single-neuron computation
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
Multifeedback-Layer Neural Network
IEEE Transactions on Neural Networks
Support Vector Echo-State Machine for Chaotic Time-Series Prediction
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
An evolutionary approach to design dilation-erosion perceptrons for stock market indices forecasting
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Shape-based template matching for time series data
Knowledge-Based Systems
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
Hybrid method for the analysis of time series gene expression data
Knowledge-Based Systems
Short communication: Selective Subsequence Time Series clustering
Knowledge-Based Systems
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
Global Artificial Bee Colony-Levenberq-Marquardt GABC-LM Algorithm for Classification
International Journal of Applied Evolutionary Computation
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In this work a class of hybrid morphological perceptrons, called dilation-erosion perceptron (DEP), is presented to overcome the random walk dilemma in the time series forecasting problem. It consists of a convex combination of fundamental operators from mathematical morphology (MM) on complete lattices theory (CLT). A gradient steepest descent method is presented to design the proposed DEP (learning process), using the back propagation (BP) algorithm and a systematic approach to overcome the problem of nondifferentiability of morphological operators. The learning process includes an automatic phase fix procedure that is geared at eliminating time phase distortions observed in some time series. Finally, an experimental analysis is conducted with the proposed DEP using five real world time series, where five well-known performance metrics and an evaluation function are used to assess the forecasting performance of the proposed model. The obtained results are compared with those generated by classical forecasting models presented in the literature.