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constructive algorithm for fully connected cascade feedforward neural networks

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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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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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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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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A constructive algorithm for feedforward neural networks with incremental training

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2002年第12期49卷 1876-1879页

作者： Liu, DR Chang, TS Zhang, Y Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

We develop, in this brief, a new constructive learning algorithm for feedforward neural networks. We employ an incremental training procedure where training patterns are learned one by one. Our algorithm starts with a single training pattern and a single hidden-layer neuron. During the course of neural network training, when the algorithm gets stuck in a local minimum, we will attempt to escape from the local minimum by using the weight scaling technique. It is only after several consecutive failed attempts in escaping from a local minimum that will we allow the network to grow by adding a hidden-layer neuron. At this stage, we employ an optimization procedure based on quadratic/linear programming to select initial weights for the newly added neuron. Our optimization procedure tends to make the network reach the error tolerance with no or little training after adding a hidden-layer neuron. Our simulation results indicate that the present constructive algorithm can obtain neural networks very close to minimal structures (with the least possible number of hidden-layer neurons) and that convergence (to a solution) in neural network training can be guaranteed. We tested our algorithm extensively using a widely used benchmark problem, i.e., the parity problem.

关键词： constructive algorithm feedforward neural networks incremental training linear programming quadratic programming

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A PDP constructive algorithm for system modelling

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NEURAL COMPUTING & APPLICATIONS 1996年第3期4卷 175-182页

作者： Crespo, JL Mora, E Applied Mathematics and Computer Science Department University of Cantabria Santander Spain

A new constructive algorithm is presented for building neural networks that learn to reproduce output temporal sequences based on one or several input sequences. This algorithm builds a network for the task of system modelling, dealing with continuous variables in the discrete rime domain. The constructive scheme makes it user independent. The network's structure consists of an ordinary set and a classification set, so it is a hybrid network like that of Stokbro et al. [6], but with a binary classification. The networks can easily be interpreted, so the learned representation can be transferred to a human engineer, unlike many other network models. This allows for a better understanding of the system structure than just its simulation. This constructive algorithm limits the network complexity automatically, hence preserving extrapolation capabilities. Examples with real data from three totally different sources show good performance and allow for a promising line of research.

关键词： neural networks constructive algorithm system modelling interpretation cross-validation hybrid networks

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A constructive algorithm for training heterogeneous neural network ensemble

A constructive algorithm for training heterogeneous neural n...

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1st International Conference on Rough Sets and Knowledge Technology (RSKT 2006)

作者： Fu, Xianghua Wang, Zhiqiang Feng, Boqin Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

ISBN: (纸本)3540362975

This paper presents a new algorithm to construct a neural network ensemble (NNE) based on heterogeneous component neural networks with negative correlation learning. The constructive algorithm consists of two parts: a sub-algorithm to construct best heterogeneous component neural networks with negative correlation learning dynamically (CBHNN), and a sub-algorithm to construct heterogeneous NNE with trained heterogeneous neural networks incrementally (CHNNE). The experiment results showe that HNNE is better than the traditional homological NNE method.

关键词： neural network ensemble constructive algorithm

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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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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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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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