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A new method for building single feedforward neural network models for multivariate static regression problems: a combined weight initialization and constructive algorithm

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EVOLUTIONARY INTELLIGENCE 2024年第2期17卷 1221-1233页

作者： de Sa, Ghabriel A. Gomes Fontes, Cristiano Hora Embirucu, Marcelo Univ Fed Bahia Ind Engn Program Polytechn Inst Salvador BA Brazil

The performance of models based on a Feedforward Neural Network depends strongly on the initial estimate of weights and the number of units in the hidden layers. This work presents a new method for the initialization of weights and definition of the number of hidden units in the identification of Multiple Input Single Output models associated with regression problems. The initialization strategy consists of a complete linearization of the network with only one neuron unit around an equilibrium point and the determination of the initial weights through the maximum approximation of the linearized model to the Optimal Linear Regressor whose solution can be obtained analytically. The constructive algorithm performs a gradual increase in the number of hidden units in such a way that at each training only weights associated with new hidden units are randomly initialized, while weights obtained from previous training are used as initial guess for the subsequent ones. The proposed method was compared to the classical random initialization method and to the Extreme Learning Machine (a typical gradient-free learning algorithm), both of which also involve a constructive approach to define the final network. The methods were applied to 11 real datasets widely used as benchmarks for regression problems. The proposed method performed better than the other approaches in 8-9 case studies and was able to ensure a monotonic decrease in the loss function with an increasing number of hidden units.

关键词： Regression Single feedforward neural network constructive algorithm Weight initialization Linearization

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Grafting constructive algorithm in feedforward neural network learning

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APPLIED INTELLIGENCE 2023年第10期53卷 11553-11570页

作者： Zhang, Siyuan Xie, Linbo Jiangnan Univ Sch Internet Things Engn 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China

constructive algorithm provides a gradually building mechanism by increasing nodes from zero. By this means, the neural network can independently and efficiently determine its structure. However, this mechanism has an essential issue: the algorithm that adds nodes one by one is too greedy to keep an efficient construction way and the global optimal solution may be missed. Therefore, this paper proposes a novel grafting mechanism to add block nodes of any number by training a sub-network during the construction. Then, a fast-training approach of the added block neurons is presented by selecting a small sub-network from the large initialized network and the corresponding grafting constructive algorithm (GCA) is established. To obtain a compact network structure, a fine-tuning scheme is developed according to GCA to adjust all parameters as a hybrid fashion and the hidden weights are extended to deal with matrix input in image classification. The experimental results on regression and classification tasks demonstrate that the proposed GCA can achieve a more compact network than other constructive algorithms and a faster error convergence rate than traditional gradient-based optimization algorithms.

关键词： constructive algorithm Grafting mechanism Ultra-wide network Feedforward neural network learning

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Virtual Analyzers for MI and Density Based on Neural Networks Improved through an Integrated Strategy Involving a constructive algorithm and Definition of Initial Weights

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MACROMOLECULAR REACTION ENGINEERING 2023年第4期17卷 2200066-2200066页

作者： da Silva, Adilton Lopes Fontes, Cristiano Hora Embirucu, Marcelo Linear Polyethylene Unit Braskem Co BR-42816200 Camacari BA Brazil Univ Fed Bahia Polytech Inst Ind Engn Program BR-40210630 Salvador BA Brazil

This work presents the development and validation of two virtual analyzers (density and Melt Index (MI)) for quality monitoring and control of the final product in an industrial unit of Linear Polyethylene (LPE). Both models are based on Feedforward Neural Networks which are improved through a strategy involving the initial estimation of weights and a constructive algorithm to define the number of hidden units. The initialization strategy is based on linearization of the neural model with only one hidden unit (nonlinear model) and subsequent optimization of this model by maximizing its similarity to the standard linear regression model whose solution is obtained analytically. The Initial Neural Model (INM) is then used as a starting point for a gradual increase in the number of hidden units. In a validation test involving MI and density values collected over 2 years of operation, the neural model is able to predict these properties with mean percentage errors equal to 0.81% (MI) and 0.04% (density) and determination coefficients equal to 0.970 (MI) and 0.983 (density). The population coefficient estimated in all tests involving grade transitions (0.96) shows a strong linear correlation between the proposed model and laboratory measurements.

关键词： constructive algorithm feedforward neural network linear polyethylene quality parameters virtual analyser

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An approach combining a new weight initialization method and constructive algorithm to configure a single Feedforward Neural Network for multi-class classification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 106卷 104495-104495页

作者： Fontes, Cristiano Hora Embirucu, Marcelo Univ Fed Bahia Escola Politecn Programa Engn Ind Grad Program Ind EngnPolytech Inst Salvador BA Brazil

This paper presents a new method for initializing weights in a Feedforward Neural Network (FNN) with a single hidden layer combined with a constructive approach to define the number of hidden units associated with the best classification performance. The strategy consists of defining an initial number of hidden units according to the classification problem, the linearization of the whole network around an equilibrium point and the determination of the initial weights and bias through the maximum approximation of the linearized model to the Optimal Linear Classifier (OLC) whose solution can be obtained analytically. The constructive algorithm comprises a gradual increase in the number of hidden units in such a way that at each training only the weights and bias associated with the new hidden units are initiated randomly while the weights and bias obtained from previous training are used as initial guesses. Additionally, the constructive algorithm seeks to ensure that the loss function of the trained networks decreases with the successive additions of hidden units. The proposed approach (Weight Initialization based on the Linearization of the Whole Neural Network combined with a new constructive algorithm, WILWNN-CA) is applied to synthetic and real datasets widely used as benchmark for multi-class classification problems. The comparison with conventional random weight initialization and other approaches involving different network topologies (and initialization strategies) shows that the proposed method is efficient and capable of providing success rates (correct classification rates) higher or similar to those achieved with existing methods.

关键词： Classification Single Feedforward Neural Network constructive algorithm Weight initialization Linearization

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constructive algorithm for fully connected cascade feedforward neural networks

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NEUROCOMPUTING 2016年 182卷 154-164页

作者： Qiao, Junfei Li, Fanjun Han, Honggui Li, Wenjing Beijing Univ Technol Coll Elect Informat & Control Engn Beijing 100124 Peoples R China Univ Jinan Sch Math Sci Shandong 250022 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

In this paper, a novel constructive algorithm, named fast cascade neural network (FCNN), is proposed to design the fully connected cascade feedforward neural network (FCCFNN). First, a modified index, based on the orthogonal least square method, is derived to select new hidden units from candidate pools. Each hidden unit leads to the maximal reduction of the sum of squared errors. Secondly, the input weights and biases of hidden units are randomly generated and remain unchanged during the learning process. The weights, which connect the input and hidden units with the output units, are calculated after all necessary units have been added. Thirdly, the convergence of FCNN is guaranteed in theory. Finally, the performance of FCNN is evaluated on some artificial and real-world benchmark problems. Simulation results show that the proposed FCNN algorithm has better generalization performance and faster learning speed than some existing algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： constructive algorithm Feedforward neural network Cascade correlation network Convergence Orthogonal least squares

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A Novel constructive algorithm for CANet

A Novel Constructive Algorithm for CANet

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International Joint Conference on Neural Networks

作者： Danilo C. Pereira Bruno J. T. Fernandes Polytechnic School of Pernambuco University of Pernambuco

ISBN: (纸本)9781509061839

Theories inspired by the working and the structure of the human brain have been applied in various problems in computer vision, such as Artificial Neural Networks (ANNs). The concept of receptive and inhibitory fields have been adopted with success to improve the capability of the ANNs and are present in the deep learning models, such as ConvNet and LIPNet, that have been successfully used in many computer vision tasks. However, both shallow and deep ANN models need some expertise to define their architecture, as well as the definition of some of their parameters. A recently introduced ANN, called CANet, embeds the concepts of receptive fields, lateral inhibition, and autoassociative memory using a constructive algorithm that requires few parameters to perform its learning process. This paper presents a new constructive-pruning algorithm for CANet that contains even fewer parameters and can self-choose the quantity of neurons in the constructive layer of the model, called CANet-2. Also, we analyze the behavior of the model with the use of activation functions from the ReLU family in its constructive layer. Experiments in facial expression recognition showed that the proposed constructive algorithm along with the SoftPlus activation function improved CANet-2 in relation to the original version.

关键词： constructive algorithm images computer vision Artificial neural networks wetware role model Model structures constructivity

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constructive algorithm for path-width of matroids 16

Constructive algorithm for path-width of matroids

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Jisu Jeong Eun Jung Kim Sang-il Oum Department of Mathematical Sciences KAIST CNRS LAMSADE-Universite Paris Dauphine Place du Marechal de Lattre de Tassigny

ISBN: (纸本)9781510819672

Given n subspaces of a finite-dimensional vector space over a fixed finite field F, we wish to find a linear layout V_1, V_2, ..., V_n of the subspaces such that dim((V_1+V_2+... + V_i)n(V_(i+1)+... +V_n)) ≤ k for all i; such a linear layout is said to have width at most k. When restricted to 1-dimensional subspaces, this problem is equivalent to computing the path-width of an F-represented matroid in matroid theory and computing the trellis-width (or minimum trellis statecomplexity) of a linear code in coding theory. We present a fixed-parameter tractable algorithm to construct a linear layout of width at most k, if it exists, for input subspaces of a finite-dimensional vector space over F. As corollaries, we obtain a fixed-parameter tractable algorithm to produce a path-decomposition of width at most k for an input F-represented matroid of path-width at most k, and a fixed-parameter tractable algorithm to find a linear rank-decomposition of width at most k for an input graph of linear rank-width at most k. In both corollaries, no such algorithms were known previously. Our approach is based on dynamic programming combined with the idea developed by Bodlaender and Kloks (1996) for their work on path-width and tree-width of graphs. It was previously known that a fixed-parameter tractable algorithm exists for the decision version of the problem for matroid path-width; a theorem by Geelen, Gerards, and Whittle (2002) implies that for each fixed finite field F, there are finitely many forbidden F-representable minors for the class of matroids of path-width at most k. An algorithm by Hlineny (2006) can detect a minor in an input F-represented matroid of bounded branch-width. However, this indirect approach would not produce an actual pathdecomposition even if the complete list of forbidden minors were known. Our algorithm is the first one to construct such a path-decomposition and does not depend on the finiteness of forbidden minors.

关键词： Matroid constructive algorithm Minors Subspace algorithms Potassium Shannon's theory layouts Fluorine (F)

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HAPE3D—a new constructive algorithm for the 3D irregular packing problem

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Frontiers of Information Technology & Electronic Engineering 2015年第5期16卷 380-390页

作者： Xiao LIU Jia-min LIU An-xi CAO Zhuang-le YAO School of Civil and Transportation Engineering South China University of Technology School of Information Science and Engineering Shenyang University of Technology College of Ocean Science and Engineer Shanghai Maritime University

We propose a new constructive algorithm, called HAPE3 D, which is a heuristic algorithm based on the principle of minimum total potential energy for the 3D irregular packing problem, involving packing a set of irregularly shaped polyhedrons into a box-shaped container with fixed width and length but unconstrained height. The objective is to allocate all the polyhedrons in the container, and thus minimize the waste or maximize profit. HAPE3 D can deal with arbitrarily shaped polyhedrons, which can be rotated around each coordinate axis at different angles. The most outstanding merit is that HAPE3 D does not need to calculate no-fit polyhedron(NFP), which is a huge obstacle for the 3D packing problem. HAPE3 D can also be hybridized with a meta-heuristic algorithm such as simulated annealing. Two groups of computational experiments demonstrate the good performance of HAPE3 D and prove that it can be hybridized quite well with a meta-heuristic algorithm to further improve the packing quality.

关键词： 3D packing problem Layout design Simulation Optimization constructive algorithm Meta-heuristics

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A constructive algorithm for Neural Networks Inspired on Decision Trees and Evolutionary algorithms

A Constructive Algorithm for Neural Networks Inspired on Dec...

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International Joint Conference on Neural Networks

作者： Marcus Vinicius Mazega Figueredo Emerson Cabrera Paraiso Julio Cesar Nievola PPGIA - PUCPR PPGIA-PUCPR

ISBN: (纸本)9781509006212

Inspired on decision trees and evolutionary algorithms, this paper proposes a learning algorithm of constructive neural networks that relies on three principles: to layout the neurons in a tree-like structure;to train each neuron individually;and, to optimize all the weights using an evolutionary approach. This way, it is expected to advance in two main questions concerning multilayer perceptrons (MLPs): how to determine the network architecture and how to build models that are more comprehensible. Based on the normalized information gain of each attribute, the algorithm builds the network architecture. In the process, it automatically creates a set of training examples for each individual neuron and executes single-cell learning. Once the network is created and trained, particle swarm optimization is utilized to evolve the connections of the network. Five metrics were utilized to validate the method when compared to decision trees and MLPs: accuracy, sensitivity, specificity, precision and comprehensibility. The experiments were executed in thirteen different databases and the results suggest that the proposed algorithm can generate neural networks with good classification performance and more comprehensible.

关键词： Knowledge extraction from neural networks Decision trees Evolutionary algorithms Decision Trees evolutionary algorithm NETWORK STRUCTURE Neural network constructive algorithm information gain Neurons Respiratory Inspiration learning algorithms

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A constructive algorithm for Yard Allocation Problem

A Constructive Algorithm for Yard Allocation Problem

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CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

作者： Ruiz, Diego Cubillos, Claudio Cabrera-Paniagua, Daniel Cubillos, Alonso Pontificia Univ Catolica Valparaiso Escuela Ingn Informat Av Brasil 2241 Valparaiso Chile Univ Valparaiso Escuela Ingn Comercial Vina Del Mar Chile

ISBN: (纸本)9781467387569

Yard Allocation Problem (YAP) is a frequently problem observed in port logistic operations, and consists in a temporary assignment of cargo containers, both export and import, to blocks storage located in the operation zone of seaports. The principal objective of YAP resolution is to improve the operations efficiency both terminal port as shipping companies that use the seaport, which allow to decrease operational cost and increasing the global efficiency. In this sense, this work proposes to solve the YAP using a constructive algorithm based in restrictions for each container. Official data of Arica Seaport - Chile were used for defining an experimental scenario, which allowed obtaining promising results. The constructive algorithm represents a novel approach because allows getting solutions in a simple and fast way, while satisfying the existing set of constrains associated with the general operation model.

关键词： Yard Allocation Problem constructive algorithm container

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