Relevant bibliographies by topics / Growing-pruning algorithm

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Academic literature on the topic 'Growing-pruning algorithm'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Growing-pruning algorithm"

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Wang, Ruliang, Huanlong Sun, Benbo Zha, and Lei Wang. "Research and Application of Improved AGP Algorithm for Structural Optimization Based on Feedforward Neural Networks." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/481919.

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The adaptive growing and pruning algorithm (AGP) has been improved, and the network pruning is based on the sigmoidal activation value of the node and all the weights of its outgoing connections. The nodes are pruned directly, but those nodes that have internal relation are not removed. The network growing is based on the idea of variance. We directly copy those nodes with high correlation. An improved AGP algorithm (IAGP) is proposed. And it improves the network performance and efficiency. The simulation results show that, compared with the AGP algorithm, the improved method (IAGP) can quickl
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Gelfand, S. B., C. S. Ravishankar, and E. J. Delp. "An iterative growing and pruning algorithm for classification tree design." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 2 (1991): 163–74. http://dx.doi.org/10.1109/34.67645.

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Zhang, Yunong, Ying Wang, Weibing Li, Yao Chou, and Zhijun Zhang. "WASD Algorithm with Pruning-While-Growing and Twice-Pruning Techniques for Multi-Input Euler Polynomial Neural Network." International Journal on Artificial Intelligence Tools 25, no. 02 (2016): 1650007. http://dx.doi.org/10.1142/s021821301650007x.

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Differing from the conventional back-propagation (BP) neural networks, a novel multi-input Euler polynomial neural network, in short, MIEPNN (specifically, 4-input Euler polynomial neural network, 4IEPNN) is established and investigated in this paper. In order to achieve satisfactory performance of the established MIEPNN, a weights and structure determination (WASD) algorithm with pruning-while-growing (PWG) and twice-pruning (TP) techniques is built up for the established MIEPNN. By employing the weights direct determination (WDD) method, the WASD algorithm not only determines the optimal con
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Han, Hong-Gui, Shuo Zhang, and Jun-Fei Qiao. "An adaptive growing and pruning algorithm for designing recurrent neural network." Neurocomputing 242 (June 2017): 51–62. http://dx.doi.org/10.1016/j.neucom.2017.02.038.

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Zemouri, Ryad, Nabil Omri, Farhat Fnaiech, Noureddine Zerhouni, and Nader Fnaiech. "A new growing pruning deep learning neural network algorithm (GP-DLNN)." Neural Computing and Applications 32, no. 24 (2019): 18143–59. http://dx.doi.org/10.1007/s00521-019-04196-8.

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Мельниченко, А. В., and К. А. Здор. "INCORPORATING ATTENTION SCORE TO IMPROVE FORESIGHT PRUNING ON TRANSFORMER MODELS." Visnyk of Zaporizhzhya National University Physical and Mathematical Sciences, no. 2 (December 19, 2023): 22–28. http://dx.doi.org/10.26661/2786-6254-2023-2-03.

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With rapid development of technologies and growing number of application of neural networks, the problem of optimization arises. Among other methods to optimize training and inference time, neural network pruning has attracted attention in recent years. The main goal of pruning is to reduce the computational complexity of neural network models while retaining performance metrics on desired level. Among the various approaches to pruning, Single-shot Network Pruning (SNIP) methods was designed as a straightforward and effective approach to optimize number of parameters before training. However,
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Lee, Chien-Cheng, and Cheng-Yuan Shih. "LEARNING PATTERNS OF LIVER MASSES USING IMPROVED RBF NETWORKS." Biomedical Engineering: Applications, Basis and Communications 22, no. 02 (2010): 137–47. http://dx.doi.org/10.4015/s1016237210001852.

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This study proposes a diagnosis system for liver masses based on the improved radial basis function (RBF) neural networks. In this article, RBF networks are improved by sigmoid function and the growing and pruning algorithm. The proposed improved RBF networks adopt the sigmoid function as their kernel due to its increased flexibility over the Gaussian kernel. Furthermore, the growing and pruning algorithm is used to adjust the network size dynamically according to the neuron's significance. This investigation formulates discriminating among cysts, hepatoma, cavernous hemangioma, and normal tis
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Chaurasia, Satvik, and R. Shobana. "An Adaptive Growing Pruning Algorithm to Optimize Dynamic Feed Forward Neural Networks for Nonlinear Dynamic System Identification." International Journal of Microsystems and IoT 3, no. 1 (2025): 1519–25. https://doi.org/10.5281/zenodo.15493712.

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Optimizing the structure is very crucial for effective identification and control of any nonlinear system. Optimization leads to a robust and more generalized structure. In this work, an effective adaptive growing-pruning algorithm scheme is proposed to optimize dynamic feed forward structures. The hidden layer of the static FFNN is made dynamic and the weights of the dynamic FFNN are trained using standard Back propagation algorithm. Firstly, the network is grown only when the MSE is found high and increasing. Likewise, unnecessarily neurons are pruned based on low activation variance. The le
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Liu, Chenyu, Dongliang Zhang, and Wankai Li. "Crown Growth Optimizer: An Efficient Bionic Meta-Heuristic Optimizer and Engineering Applications." Mathematics 12, no. 15 (2024): 2343. http://dx.doi.org/10.3390/math12152343.

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This paper proposes a new meta-heuristic optimization algorithm, the crown growth optimizer (CGO), inspired by the tree crown growth process. CGO innovatively combines global search and local optimization strategies by simulating the growing, sprouting, and pruning mechanisms in tree crown growth. The pruning mechanism balances the exploration and exploitation of the two stages of growing and sprouting, inspired by Ludvig’s law and the Fibonacci series. We performed a comprehensive performance evaluation of CGO on the standard testbed CEC2017 and the real-world problem set CEC2020-RW and compa
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Wang, Qitong, Ioana Ileana, and Themis Palpanas. "LeaFi: Data Series Indexes on Steroids with Learned Filters." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–27. https://doi.org/10.1145/3709701.

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The ever-growing collections of data series create a pressing need for efficient similarity search, which serves as the backbone for various analytics pipelines. Recent studies have shown that tree-based series indexes excel in many scenarios. However, we observe a significant waste of effort during search, due to suboptimal pruning. To address this issue, we introduce LeaFi, a novel framework that uses machine learning models to boost pruning effectiveness of tree-based data series indexes. These models act as learned filters, which predict tight node-wise distance lower bounds that are used
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Book chapters on the topic "Growing-pruning algorithm"

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Pavunraj, D., A. Mathankumar, K. Anbumaheshwari, and R. Daisy Merina. "Green Artificial Intelligence (AI) and Machine Learning (ML)." In Energy Efficient Algorithms and Green Data Centers for Sustainable Computing. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0766-4.ch010.

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Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of technological progress, revolutionizing fields such as healthcare, finance, and transportation. However, the energy-intensive processes involved in training and deploying complex AI models have raised significant environmental concerns. Green AI and ML represent a shift toward sustainable practices by emphasizing energy efficiency, reducing carbon footprints, and minimizing resource usage. This approach encompasses the development of optimized algorithms, efficient hardware solutions, and the use of renewable energy
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D, Saravanan, and S. Pragadeeswaran. "Optimized Hardware Acceleration of Deep Learning Algorithms Using Xilinx Zynq SoCs." In Smart Microcontrollers and FPGA Based Architectures for Advanced Computing and Signal Processing, 2025th ed. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552425-03.

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The growing demand for intelligent edge computing has intensified the need for deploying deep learning models on energy-efficient and performance-constrained platforms. Xilinx Zynq Systemon-Chip (SoC) devices offer a unique architectural blend of programmable logic and embedded processors, enabling customized hardware acceleration for artificial intelligence applications. This book chapter explores advanced techniques in model quantization and structured pruning to optimize neural networks for inference on Zynq-based systems. It provides an in-depth analysis of co-design strategies, layer sens
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Conference papers on the topic "Growing-pruning algorithm"

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Guang-Bin Huang, P. Saratchandran, and N. Sundararajan. "A Recursive Growing and Pruning RBF (GAP-RBF) Algorithm for Function Approximations." In 4th International Conference on Control and Automation. Final Program and Book of Abstracts. IEEE, 2003. http://dx.doi.org/10.1109/icca.2003.1595070.

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Dora, Shirin, Suresh Sundaram, and Narasimhan Sundararajan. "A two stage learning algorithm for a Growing-Pruning Spiking Neural Network for pattern classification problems." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280592.

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Luo, Run, Shifa Wu, Xinyu Wei, and Fuyu Zhao. "Identification Modeling of Accelerator Driven System Based on Growing and Pruning Radial Basis Function Network." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60328.

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An identification method based on growing and pruning radial basis function network (GAP-RBFN) is presented for modeling an accelerator driven system (ADS). Compared with traditional neural networks, GAP-RBFN could automatically adjust the number of hidden neurons to find a suitable network structure by using growing and pruning strategies. In addition, an extended Kalman filter (EKF) algorithm is adopted to update network parameters of neurons in GAP-RBFN, which has a rapid convergence speed during the training process. A numerical calculation code named ARTAP (ADS Reactor Transient Analysis
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Tong Seng Quah and Kian-Chong Wong. "Utilizing Generalized Growing and Pruning Algorithm for Radial Basis Function (GGAP-RBF) Network in Predicting IPOs Performance." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247258.

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Shankar, Praveen, and Rama K. Yedavalli. "A Neural Network Based Adaptive Observer for Turbine Engine Parameter Estimation." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-90603.

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Estimation of immeasurable parameters such as thrust and turbine inlet temperatures in turbine engines constitutes a significant challenge for the aircraft community. A solution to this problem is to estimate these parameters from the measured outputs using an observer. Currently existing technologies rely on Kalman and extended Kalman filters to achieve this estimation. This paper presents an adaptive observer that augments the linear Kalman filter with a neural network to compensate for any nonlinearity that is not handled by the linear filter. The neural network implemented is a Radial Basi
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Kurata, Masahiro, Jun-Hee Kim, Jerome P. Lynch, Kincho H. Law, and Liming W. Salvino. "A Probabilistic Model Updating Algorithm for Fatigue Damage Detection in Aluminum Hull Structures." In ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3838.

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The use of aluminum alloys in the design of naval structures offers the benefit of light-weight ships that can travel at high-speed. However, the use of aluminum poses a number of challenges for the naval engineering community including higher incidence of fatigue-related cracks. Early detection of fatigue induced cracks enhances maintenance of the ships and is critical for preventing the catastrophic failure of the hull. Furthermore, monitoring the integrity of the aluminum hull can provide valuable information for estimating the residual life of hull components. This paper presents a model-b
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Zhang, Yipeng, Bo Du, Lefei Zhang, Rongchun Li, and Yong Dou. "Accelerated Inference Framework of Sparse Neural Network Based on Nested Bitmask Structure." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/605.

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In order to satisfy the ever-growing demand for high-performance processors for neural networks, the state-of-the-art processing units tend to use application-oriented circuits to replace Processing Engine (PE) on the GPU under circumstances where low-power solutions are required. The application-oriented PE is fully optimized in terms of the circuit architecture and eliminates incorrect data dependency and instructional redundancy. In this paper, we propose a novel encoding approach on a sparse neural network after pruning. We partition the weight matrix into numerous blocks and use a low-ran
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Vempati, Chaitanya, and Matthew I. Campbell. "A Graph Grammar Approach to Generate Neural Network Topologies." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34588.

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Neural networks are increasingly becoming a useful and popular choice for process modeling. The success of neural networks in effectively modeling a certain problem depends on the topology of the neural network. Generating topologies manually relies on previous neural network experience and is tedious and difficult. Hence there is a rising need for a method that generates neural network topologies for different problems automatically. Current methods such as growing, pruning and using genetic algorithms for this task are very complicated and do not explore all the possible topologies. This pap
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