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Solano Meza JK, Orjuela Yepes D, Rodrigo-Ilarri J, Rodrigo-Clavero ME. Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4256. [PMID: 36901265 PMCID: PMC10002305 DOI: 10.3390/ijerph20054256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method.
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Affiliation(s)
- Johanna Karina Solano Meza
- Department of Environmental Engineering, Santo Tomás University, Road 9 Street 51-11, Bogotá 110231, Colombia
| | - David Orjuela Yepes
- Department of Environmental Engineering, Santo Tomás University, Road 9 Street 51-11, Bogotá 110231, Colombia
| | - Javier Rodrigo-Ilarri
- Instituto de Ingeniería del Agua y Medio Ambiente (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain
| | - María-Elena Rodrigo-Clavero
- Instituto de Ingeniería del Agua y Medio Ambiente (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain
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Goel A, Goel AK, Kumar A. Performance analysis of multiple input single layer neural network hardware chip. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-22. [PMID: 36846531 PMCID: PMC9939870 DOI: 10.1007/s11042-023-14627-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/24/2022] [Accepted: 02/03/2023] [Indexed: 06/18/2023]
Abstract
An artificial neural network (ANN) is a computational system that is designed to replicate and process the behavior of the human brain using neuron nodes. ANNs are made up of thousands of processing neurons with input and output modules that self-learn and compute data to offer the best results. The hardware realization of the massive neuron system is a difficult task. The research article emphasizes the design and realization of multiple input perceptron chips in Xilinx integrated system environment (ISE) 14.7 software. The proposed single-layer ANN architecture is scalable and accepts variable 64 inputs. The design is distributed in eight parallel blocks of ANN in which one block consists of eight neurons. The performance of the chip is analyzed based on the hardware utilization, memory, combinational delay, and different processing elements with targeted hardware Virtex-5 field-programmable gate array (FPGA). The chip simulation is performed in Modelsim 10.0 software. Artificial intelligence has a wide range of applications, and cutting-edge computing technology has a vast market. Hardware processors that are fast, affordable, and suited for ANN applications and accelerators are being developed by the industries. The novelty of the work is that it provides a parallel and scalable design platform on FPGA for fast switching, which is the current need in the forthcoming neuromorphic hardware.
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Affiliation(s)
- Akash Goel
- Department of Computer Science & Engineering, Galgotia’s University, Greater Noida, NCR India
| | - Amit Kumar Goel
- Department of Computer Science & Engineering, Galgotia’s University, Greater Noida, NCR India
| | - Adesh Kumar
- Department of Electrical & Electronics Engineering, University of Petroleum and Energy Studies, Dehradun, India
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Bilski J, Rutkowski L, Smoląg J, Tao D. A novel method for speed training acceleration of recurrent neural networks. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.10.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Krestinskaya O, James AP, Chua LO. Neuromemristive Circuits for Edge Computing: A Review. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:4-23. [PMID: 30892238 DOI: 10.1109/tnnls.2019.2899262] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.
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Che H, Wang J. A Two-Timescale Duplex Neurodynamic Approach to Biconvex Optimization. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:2503-2514. [PMID: 30602424 DOI: 10.1109/tnnls.2018.2884788] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper presents a two-timescale duplex neurodynamic system for constrained biconvex optimization. The two-timescale duplex neurodynamic system consists of two recurrent neural networks (RNNs) operating collaboratively at two timescales. By operating on two timescales, RNNs are able to avoid instability. In addition, based on the convergent states of the two RNNs, particle swarm optimization is used to optimize initial states of the RNNs to avoid local minima. It is proven that the proposed system is globally convergent to the global optimum with probability one. The performance of the two-timescale duplex neurodynamic system is substantiated based on the benchmark problems. Furthermore, the proposed system is applied for L1 -constrained nonnegative matrix factorization.
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Mateo J, Torres AM, García MA, Santos JL. Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-1988-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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A robust recurrent simultaneous perturbation stochastic approximation training algorithm for recurrent neural networks. Neural Comput Appl 2013. [DOI: 10.1007/s00521-013-1436-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Okamoto T, Hirata H. Global optimization using a multipoint type quasi-chaotic optimization method. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2012.10.025] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Statistical Information Based Single Neuron Adaptive Control for Non-Gaussian Stochastic Systems. ENTROPY 2012. [DOI: 10.3390/e14071154] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Jie Lian, Zhi Feng, Peng Shi. Observer Design for Switched Recurrent Neural Networks: An Average Dwell Time Approach. ACTA ACUST UNITED AC 2011; 22:1547-56. [DOI: 10.1109/tnn.2011.2162111] [Citation(s) in RCA: 94] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Guo D, Yi C, Zhang Y. Zhang neural network versus gradient-based neural network for time-varying linear matrix equation solving. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.05.021] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Patel ND, Nguang SK, Coghill GG. Neural network implementation using bit streams. IEEE TRANSACTIONS ON NEURAL NETWORKS 2007; 18:1488-1504. [PMID: 18220196 DOI: 10.1109/tnn.2007.895822] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
A new method for the parallel hardware implementation of artificial neural networks (ANNs) using digital techniques is presented. Signals are represented using uniformly weighted single-bit streams. Techniques for generating bit streams from analog or multibit inputs are also presented. This single-bit representation offers significant advantages over multibit representations since they mitigate the fan-in and fan-out issues which are typical to distributed systems. To process these bit streams using ANNs concepts, functional elements which perform summing, scaling, and squashing have been implemented. These elements are modular and have been designed such that they can be easily interconnected. Two new architectures which act as monotonically increasing differentiable nonlinear squashing functions have also been presented. Using these functional elements, a multilayer perceptron (MLP) can be easily constructed. Two examples successfully demonstrate the use of bit streams in the implementation of ANNs. Since every functional element is individually instantiated, the implementation is genuinely parallel. The results clearly show that this bit-stream technique is viable for the hardware implementation of a variety of distributed systems and for ANNs in particular.
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Affiliation(s)
- Nitish D Patel
- Department of Electrical and Computer Engineering University of Auckland, Auckland 1001, New Zealand.
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Duren RW, Marks RJ, Reynolds PD, Trumbo ML. Real-time neural network inversion on the SRC-6e reconfigurable computer. IEEE TRANSACTIONS ON NEURAL NETWORKS 2007; 18:889-901. [PMID: 17526353 DOI: 10.1109/tnn.2007.891679] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Implementation of real-time neural network inversion on the SRC-6e, a computer that uses multiple field-programmable gate arrays (FPGAs) as reconfigurable computing elements, is examined using a sonar application as a specific case study. A feedforward multilayer perceptron neural network is used to estimate the performance of the sonar system (Jung et al., 2001). A particle swarm algorithm uses the trained network to perform a search for the control parameters required to optimize the output performance of the sonar system in the presence of imposed environmental constraints (Fox et al., 2002). The particle swarm optimization (PSO) requires repetitive queries of the neural network. Alternatives for implementing neural networks and particle swarm algorithms in reconfigurable hardware are contrasted. The final implementation provides nearly two orders of magnitude of speed increase over a state-of-the-art personal computer (PC), providing a real-time solution.
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Affiliation(s)
- Russell W Duren
- Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA.
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Savich AW, Moussa M, Areibi S. The Impact of Arithmetic Representation on Implementing MLP-BP on FPGAs: A Study. ACTA ACUST UNITED AC 2007; 18:240-52. [PMID: 17278475 DOI: 10.1109/tnn.2006.883002] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper, arithmetic representations for implementing multilayer perceptrons trained using the error backpropagation algorithm (MLP-BP) neural networks on field-programmable gate arrays (FPGAs) are examined in detail. Both floating-point (FLP) and fixed-point (FXP) formats are studied and the effect of precision of representation and FPGA area requirements are considered. A generic very high-speed integrated circuit hardware description language (VHDL) program was developed to help experiment with a large number of formats and designs. The results show that an MLP-BP network uses less clock cycles and consumes less real estate when compiled in an FXP format, compared with a larger and slower functioning compilation in an FLP format with similar data representation width, in bits, or a similar precision and range.
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Affiliation(s)
- Antony W Savich
- School of Engineering, University of Guelph, Guelph, ON NIG 2W1, Canada
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