Journal articles: 'Forward-And-Backward algorithm'

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Wang, Feng, Jianping Zhang, Guiling Sun, and Tianyu Geng. "Iterative Forward-Backward Pursuit Algorithm for Compressed Sensing." Journal of Electrical and Computer Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5940371.

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It has been shown that iterative reweighted strategies will often improve the performance of many sparse reconstruction algorithms. Iterative Framework for Sparse Reconstruction Algorithms (IFSRA) is a recently proposed method which iteratively enhances the performance of any given arbitrary sparse reconstruction algorithm. However, IFSRA assumes that the sparsity level is known. Forward-Backward Pursuit (FBP) algorithm is an iterative approach where each iteration consists of consecutive forward and backward stages. Based on the IFSRA, this paper proposes the Iterative Forward-Backward Pursuit (IFBP) algorithm, which applies the iterative reweighted strategies to FBP without the need for the sparsity level. By using an approximate iteration strategy, IFBP gradually iterates to approach the unknown signal. Finally, this paper demonstrates that IFBP significantly improves the reconstruction capability of the FBP algorithm, via simulations including recovery of random sparse signals with different nonzero coefficient distributions in addition to the recovery of a sparse image.

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Renugadevi, S., Aayush S. Rawat, Suraj Swaminathan, P. Arulmozhivarman, and R. Selvakumar. "Perimeter coverage using backward and forward greedy algorithm." Applied Mathematical Sciences 7 (2013): 3883–96. http://dx.doi.org/10.12988/ams.2013.34203.

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Moursi, Walaa M. "The Forward–Backward Algorithm and the Normal Problem." Journal of Optimization Theory and Applications 176, no. 3 (February 6, 2018): 605–24. http://dx.doi.org/10.1007/s10957-017-1113-4.

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Chen, Jinjian, Xingyu Luo, Yuchao Tang, and Qiaoli Dong. "Primal-Dual Splitting Algorithms for Solving Structured Monotone Inclusion with Applications." Symmetry 13, no. 12 (December 13, 2021): 2415. http://dx.doi.org/10.3390/sym13122415.

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This work proposes two different primal-dual splitting algorithms for solving structured monotone inclusion containing a cocoercive operator and the parallel-sum of maximally monotone operators. In particular, the parallel-sum is symmetry. The proposed primal-dual splitting algorithms are derived from two approaches: One is the preconditioned forward–backward splitting algorithm, and the other is the forward–backward–half-forward splitting algorithm. Both algorithms have a simple calculation framework. In particular, the single-valued operators are processed via explicit steps, while the set-valued operators are computed by their resolvents. Numerical experiments on constrained image denoising problems are presented to show the performance of the proposed algorithms.

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Tottori, Takehiro, and Tetsuya J. Kobayashi. "Forward and Backward Bellman Equations Improve the Efficiency of the EM Algorithm for DEC-POMDP." Entropy 23, no. 5 (April 29, 2021): 551. http://dx.doi.org/10.3390/e23050551.

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Decentralized partially observable Markov decision process (DEC-POMDP) models sequential decision making problems by a team of agents. Since the planning of DEC-POMDP can be interpreted as the maximum likelihood estimation for the latent variable model, DEC-POMDP can be solved by the EM algorithm. However, in EM for DEC-POMDP, the forward–backward algorithm needs to be calculated up to the infinite horizon, which impairs the computational efficiency. In this paper, we propose the Bellman EM algorithm (BEM) and the modified Bellman EM algorithm (MBEM) by introducing the forward and backward Bellman equations into EM. BEM can be more efficient than EM because BEM calculates the forward and backward Bellman equations instead of the forward–backward algorithm up to the infinite horizon. However, BEM cannot always be more efficient than EM when the size of problems is large because BEM calculates an inverse matrix. We circumvent this shortcoming in MBEM by calculating the forward and backward Bellman equations without the inverse matrix. Our numerical experiments demonstrate that the convergence of MBEM is faster than that of EM.

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Jannati, Mohammad, Tole Sutikno, Nik Rumzi Nik Idris, and Mohd Junaidi Abdul Aziz. "High Performance Speed Control of Single-Phase Induction Motors Using Switching Forward and Backward EKF Strategy." International Journal of Power Electronics and Drive Systems (IJPEDS) 7, no. 1 (March 1, 2016): 17. http://dx.doi.org/10.11591/ijpeds.v7.i1.pp17-27.

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<p>The aim of this research is to provide a high performance vector control of single-phase Induction Motor (IM) drives. It is shown that in the rotating reference frame, the single-phase IM equations can be separated into forward and backward equations with the balanced structure. Based on this, a method for vector control of the single-phase IM, using two modified Rotor Field-Oriented Control (RFOC) algorithms is presented. In order to accommodate forward and backward rotor fluxes in the presented controller, an Extended Kalman Filter (EKF) with two different forward and backward currents that are switched interchangeably (switching forward and backward EKF), is proposed. Simulation results illustrate the effectiveness of the proposed algorithm.</p>

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Liu, Liya, Xiaolong Qin, and Jen-Chih Yao. "A Hybrid Forward–Backward Algorithm and Its Optimization Application." Mathematics 8, no. 3 (March 19, 2020): 447. http://dx.doi.org/10.3390/math8030447.

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In this paper, we study a hybrid forward–backward algorithm for sparse reconstruction. Our algorithm involves descent, splitting and inertial ideas. Under suitable conditions on the algorithm parameters, we establish a strong convergence solution theorem in the framework of Hilbert spaces. Numerical experiments are also provided to illustrate the application in the field of signal processing.

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Stankovic, Marko, Miroslav Ciric, and Jelena Ignjatovic. "Simulations and bisimulations for fuzzy multimodal logics over Heyting algebras." Filomat 37, no. 3 (2023): 711–43. http://dx.doi.org/10.2298/fil2303711s.

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In the present paper, we study fuzzy multimodal logics over complete Heyting algebras and Kripke models for these logics. We introduce two types of simulations (forward and backward) and five types of bisimulations (forward, backward, forward-backward, backward-forward and regular) between Kripke models, as well as the corresponding presimulations and prebisimulations, which are simulations and bisimulations with relaxed conditions. For each type of simulations and bisimulations an efficient algorithm has been provided that works as follows: it computes the greatest presimulation/prebisimulation of that type, and then checks whether it meets the additional condition: if it does, then it is also the greatest simulation/ bisimulation of that type, otherwise, there is not any simulation/bisimulation of that type. The algorithms are inspired by algorithms for checking the existence and computing the greatest simulations and bisimulations between fuzzy automata. We also demonstrate the application of these algorithms in the state reduction of Kripke models. We show that forward bisimulation fuzzy equivalences on the Kripke model provide reduced models equivalent to the original model concerning plus-formulas, backward bisimulation fuzzy equivalences provide reduced models equivalent concerning minus-formulas, while regular bisimulation fuzzy equivalences provide reduced models equivalent concerning all modal formulas.

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Bussaban, Limpapat, Attapol Kaewkhao, and Suthep Suantai. "Inertial s-iteration forward-backward algorithm for a family of nonexpansive operators with applications to image restoration problems." Filomat 35, no. 3 (2021): 771–82. http://dx.doi.org/10.2298/fil2103771b.

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Image restoration is an important branch of image processing which has been studied extensively while there are several methods to solve this problem by many authors with the challenges of computational speed and accuracy of algorithms. In this paper, we present two methods, called ?Inertial S-iteration forward-backward algorithm (ISFBA)? and ?A fast iterative shrinkage-thresholding algorithm-Siteration (FISTA-S)?, for finding an approximate solution of least absolute shrinkage and selection operator problem by using a special technique in fixed point theory and prove weak convergence of the proposed methods under some suitable conditions. Moreover, we apply our main results to solve image restoration problems. It is shown by some numerical examples that our algorithms have a good behavior compared with forward-backward algorithm (FBA), a new accelerated proximal gradient algorithm (nAGA) and a fast iterative shrinkage-thresholding algorithm (FISTA).

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MUNEHISA, TOMO, and HIDEKAZU TANAKA. "ALGORITHM DEPENDENCE OF PARTON SHOWERS IN DEEP INELASTIC SCATTERING." International Journal of Modern Physics A 06, no. 06 (March 10, 1991): 989–1002. http://dx.doi.org/10.1142/s0217751x9100054x.

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Parton showers for deep inelastic electron-proton scattering are studied. Forward and backward evolution algorithms are constructed using the same definitions for splitting and evolution variables. We discuss features of two algorithms and examine algorithm dependence of final parton distributions.

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Fotouhi-C., Reza, and Walerian Szyszkowski. "An Algorithm for Time-Optimal Control Problems." Journal of Dynamic Systems, Measurement, and Control 120, no. 3 (September 1, 1998): 414–18. http://dx.doi.org/10.1115/1.2805419.

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A numerical procedure to compute nonsingular, time-optimal solutions for nonlinear systems, which have fixed initial and final states, and are linear and bounded in control, is presented. Using the Pontryagin’s Minimum Principle, the corresponding nonlinear two-point boundary-value problem is formulated and solved by combination of the forward-backward and the shooting methods. The forward-backward procedure generates a good guess of the initial costates, which is crucial for the convergence of the shooting method. Numerical example of a two-link manipulator illustrates the proposed approach and the convergence of the procedure.

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Wei, Wu, Xu Le, Zhang Xiaofei, and Li Jianfeng. "Compressed Sensing PARALIND Decomposition-Based Coherent Angle Estimation for Uniform Rectangular Array." Wireless Communications and Mobile Computing 2019 (June 24, 2019): 1–10. http://dx.doi.org/10.1155/2019/3619818.

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In this paper, the topic of coherent two-dimensional direction of arrival (2D-DOA) estimation is investigated. Our study jointly utilizes the compressed sensing (CS) technique and the parallel profiles with linear dependencies (PARALIND) model and presents a 2D-DOA estimation algorithm for coherent sources with the uniform rectangular array. Compared to the traditional PARALIND decomposition, the proposed algorithm owns lower computational complexity and smaller data storage capacity due to the process of compression. Besides, the proposed algorithm can obtain autopaired azimuth angles and elevation angles and can achieve the same estimation performance as the traditional PARALIND, which outperforms some familiar algorithms presented for coherent sources such as the forward backward spatial smoothing-estimating signal parameters via rotational invariance techniques (FBSS-ESPRIT) and forward backward spatial smoothing-propagator method (FBSS-PM). Extensive simulations are provided to validate the effectiveness of the proposed CS-PARALIND algorithm.

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Gao, Wei, and Bochen Zhang. "Impact Load Identification with Uniform Noise Based on Forward-backward Splitting Algorithm." Journal of Physics: Conference Series 2557, no. 1 (July 1, 2023): 012053. http://dx.doi.org/10.1088/1742-6596/2557/1/012053.

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Abstract In this paper, the forward-backward splitting method is introduced for the first time to solve the impact load identification problem under the condition of uniform noise in the field of structural dynamics. In the process of using this method to solve the problem of impact load identification, firstly, the load identification model is established. Then, the model is transformed into an optimization problem with infinite norm constraints by the maximum posterior estimation, and then the forward-backward splitting method is used to solve the optimization problem. The rationality and effectiveness of the forward and backward splitting method for load identification under uniform noise conditions in the field of structural dynamics are illustrated by numerical simulation examples.

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Song, Yanlai, and Mihai Postolache. "Modified Inertial Forward–Backward Algorithm in Banach Spaces and Its Application." Mathematics 9, no. 12 (June 12, 2021): 1365. http://dx.doi.org/10.3390/math9121365.

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In this paper, we present a new modified inertial forward–backward algorithm for finding a common solution of the quasi-variational inclusion problem and the variational inequality problem in a q-uniformly smooth Banach space. The proposed algorithm is based on descent, splitting and inertial ideas. Under suitable assumptions, we prove that the sequence generated by the iterative algorithm converges strongly to the unique solution of the abovementioned problems. Numerical examples are also given to demonstrate our results.

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Julianto, Indri Tri, Dede Kurniadi, Fathia Alisha Fauziah, and Ricky Rohmanto. "Improvement of Data Mining Models using Forward Selection and Backward Elimination with Cryptocurrency Datasets." Journal of Applied Intelligent System 8, no. 1 (February 17, 2023): 100–109. http://dx.doi.org/10.33633/jais.v8i1.7568.

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Cryptocurrency is a digital currency not managed by a state or central bank, and transactions are peer-to-peer. Cryptocurrency is still considered a speculative asset and its price volatility is relatively high, but it is also expected to become an efficient and secure transaction tool in the future. The purpose of this study is to compare and improve the performance of the Data Mining Algorithm model using the Feature Selection-Wrapper with the Binance Coin (BNB) cryptocurrency dataset. The Feature Selection-Wrapper approach used is Forward Selection and Backward Elimination. The algorithms used are Neural Networks, Deep Learning, Support Vector Machines, and Linear Regression. The methodology used is Knowledge Discovery in Databases. The results showed that from a comparison using K-Fold Cross Validation with a value of K=10, the Neural Network Algorithm has the best Root Mean Square Error value of 10,734 +/- 10,124 (micro average: 14,580 +/- 0,000). Then after improving performance using Forward Selection and Backward Elimination in the Neural Network Algorithm, the best performance improvement results are shown by using Backward Elimination with RMSE 5,302 +/- 2,647 (micro average: 5,805 +/- 0,000).

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Fitrianah, Devi, and Hisyam Fahmi. "THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS." SINERGI 23, no. 3 (October 11, 2019): 184. http://dx.doi.org/10.22441/sinergi.2019.3.002.

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This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.

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Grant, P. W., J. A. Sharp, M. F. Webster, and X. Zhang. "Sparse matrix representations in a functional language." Journal of Functional Programming 6, no. 1 (January 1996): 143–70. http://dx.doi.org/10.1017/s095679680000160x.

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AbstractThis paper investigates several sparse matrix representation schemes and associated algorithms in Haskell for solving linear systems of equations arising from solving realistic computational fluid dynamics problems using a finite element algorithm. This work complements that of Wainwright and Sexton (1992) in that a Choleski direct solver (with an emphasis on its forward/backward substitution steps) is examined. Experimental evidence comparing time and space efficiency of these matrix representation schemes is reported, together with associated forward/backward substitution implementations. Our results are in general agreement with Wainwright and Sexton's.

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Moysis, Lazaros, and Nicholas P. Karampetakis. "Construction of Algebraic and Difference Equations with a Prescribed Solution Space." International Journal of Applied Mathematics and Computer Science 27, no. 1 (March 28, 2017): 19–32. http://dx.doi.org/10.1515/amcs-2017-0002.

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Abstract This paper studies the solution space of systems of algebraic and difference equations, given as auto-regressive (AR) representations A(σ)β(k) = 0, where σ denotes the shift forward operator and A(σ) is a regular polynomial matrix. The solution space of such systems consists of forward and backward propagating solutions, over a finite time horizon. This solution space can be constructed from knowledge of the finite and infinite elementary divisor structure of A(σ). This work deals with the inverse problem of constructing a family of polynomial matrices A(σ) such that the system A(σ)β(k) = 0 satisfies some given forward and backward behavior. Initially, the connection between the backward behavior of an AR representation and the forward behavior of its dual system is showcased. This result is used to construct a system satisfying a certain backward behavior. By combining this result with the method provided by Gohberg et al. (2009) for constructing a system with a forward behavior, an algorithm is proposed for computing a system satisfying the prescribed forward and backward behavior.

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Liu, Haiqiang, Gang Hua, Aichun Zhu, Hongsheng Yin, and Yonggang Xu. "A Hybrid Orthogonal Forward-Backward Pursuit Algorithm for Partial Fourier Multiple Measurement Vectors Problem." Mathematical Problems in Engineering 2018 (July 17, 2018): 1–12. http://dx.doi.org/10.1155/2018/5965020.

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In solving the partial Fourier Multiple Measurement Vectors (FMMV) problem, existing greedy pursuit algorithms such as Simultaneous Orthogonal Matching Pursuit (SOMP), Simultaneous Subspace Pursuit (SSP), Hybrid Matching Pursuit (HMP), and Forward-Backward Pursuit (FBP) suffer from low recovery ability or need sparsity as a prior information. This paper combines SOMP and FBP to propose a Hybrid Orthogonal Forward-Backward Pursuit (HOFBP) algorithm. As an iterative algorithm, each iteration of HOFBP consists of two stages. In the first stage, α indices selected by SOMP are added to the support set. In the second stage, the support set is shrank by removing β indices. The choice of α and β is critical to the performance of this algorithm. The simulation results showed that, by using proper parameters, HOFBP has better performance than other greedy pursuit algorithms at the expense of more computing time in some cases. HOFBP does not need sparsity as a prior knowledge.

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Boţ, Radu Ioan, Panayotis Mertikopoulos, Mathias Staudigl, and Phan Tu Vuong. "Minibatch Forward-Backward-Forward Methods for Solving Stochastic Variational Inequalities." Stochastic Systems 11, no. 2 (June 2021): 112–39. http://dx.doi.org/10.1287/stsy.2019.0064.

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We develop a new stochastic algorithm for solving pseudomonotone stochastic variational inequalities. Our method builds on Tseng’s forward-backward-forward algorithm, which is known in the deterministic literature to be a valuable alternative to Korpelevich’s extragradient method when solving variational inequalities over a convex and closed set governed by pseudomonotone Lipschitz continuous operators. The main computational advantage of Tseng’s algorithm is that it relies only on a single projection step and two independent queries of a stochastic oracle. Our algorithm incorporates a minibatch sampling mechanism and leads to almost sure convergence to an optimal solution. To the best of our knowledge, this is the first stochastic look-ahead algorithm achieving this by using only a single projection at each iteration.

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Thilaganga, V., M. Karthika, and M. Maha Lakshmi. "A Prefetching Technique Using HMM Forward and Backward Chaining for the DFS in Cloud." Asian Journal of Computer Science and Technology 6, no. 2 (November 5, 2017): 23–26. http://dx.doi.org/10.51983/ajcst-2017.6.2.1784.

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A general class of temporal probabilistic model have recently developed, which extends the Forward, Backward and Viterbi algorithm for hidden Markov models. The HMM (Midden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The algorithm is based on shrinking the state space of the HMM noticeably using such chains. The states through which the world passes are hidden, or unobserved. However, at each point in time also gets an observation that in some way reflects on the current state of the world. The Cloud Computing is a big deal for three reasons: It does not need any effort on Clients part to maintain or manage. It’s effectively infinite in size, so clients don’t need to worry about it running out of capacity. User can access cloud-based applications and services from anywhere all you need is a device with an Internet connection. In this Cloud Computing used the Distributed File Systems (DFS) for sharing and allocating the data during dynamic process .Those process are using some Prediction algorithms here using HMM Forward and Backward Chain. In this paper represents, Cloud Storage Server can Share the data among with the multiple users, using two prediction algorithms such as forward and backward chain in HMM.

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Laverty, William H., and Ivan W. Kelly. "Visualizing State Identification in Auto-Regressive Hidden Markov (ARHMM) Models With the Forward and Backward Algorithms Using Excel." International Journal of Statistics and Probability 8, no. 5 (August 6, 2019): 25. http://dx.doi.org/10.5539/ijsp.v8n5p25.

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Earlier articles, Laverty, Miket, Kelly (2002c), Laverty and Kelly (2019) used Excel to simulate Hidden Markov models and calculate the probabilities of the unknown states using the forward and backward algorithms (Rabiner, 1989). In those articles, independence between observations in each state were assumed. In many situations, however, the assumption of independence within states cannot be made. A more appropriate model for the data in this case would be an Autoregressive Hidden Markov model which accounts for serial correlation within states. In this article, a two-state ARHMM will be simulated with the forward-backward algorithm used to calculate conditional state probabilities given the observed data.

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Stefanoiu, Anca, Andreas Weinmann, Martin Storath, Nassir Navab, and Maximilian Baust. "Joint Segmentation and Shape Regularization With a Generalized Forward–Backward Algorithm." IEEE Transactions on Image Processing 25, no. 7 (July 2016): 3384–94. http://dx.doi.org/10.1109/tip.2016.2567068.

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He, Tao, Yasheng Sun, Biao Chen, Jin Qi, Wenhai Liu, and Jie Hu. "Plug-and-play inertial forward–backward algorithm for Poisson image deconvolution." Journal of Electronic Imaging 28, no. 04 (July 30, 2019): 1. http://dx.doi.org/10.1117/1.jei.28.4.043020.

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Wang, Yuanheng, Miaoqing Li, Chengru Yao, and Bingnan Jiang. "Two New Modified Regularized Methods for Solving the Variational Inclusion and Null Point Problems." Mathematics 11, no. 6 (March 17, 2023): 1469. http://dx.doi.org/10.3390/math11061469.

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In this article, based on the regularization techniques, we construct two new algorithms combining the forward-backward splitting algorithm and the proximal contraction algorithm, respectively. Iterative sequences of the new algorithms can converge strongly to a common solution of the variational inclusion and null point problems in real Hilbert spaces. Multi-inertial extrapolation steps are applied to expedite their convergence rate. We also give some numerical experiments to certify that our algorithms are viable and efficient.

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LAN, JAMES K., WELL Y. CHOU, and CHIUYUAN CHEN. "EFFICIENT ROUTING ALGORITHMS FOR GENERALIZED SHUFFLE-EXCHANGE NETWORKS." Discrete Mathematics, Algorithms and Applications 01, no. 02 (June 2009): 267–81. http://dx.doi.org/10.1142/s179383090900021x.

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The shuffle-exchange network has been proposed as a popular architecture for multistage interconnection networks. In 1991, Padmanabhan introduced the generalized shuffle-exchange network (GSEN) and proposed an efficient routing algorithm. Later, Chen et al. further enhanced the GSEN with bidirectional links and proposed the bidirectional GSEN (BGSEN). A BGSEN consists of the forward and the backward network. Based on the idea of inversely using the control tag generated by Padmanabhan's algorithm, Chen et al. proposed a routing algorithm for the backward network. Recently, Chen and Lou also proposed a routing algorithm for the backward network. It should be noted, however, that Padmanabhan's algorithm is actually an explicit formula for computing the control tag for routing and takes only O(1) time. Neither the algorithm of Chen et al. nor the algorithm of Chen and Lou provides an explicit formula for computing the control tag for routing and both algorithms take at least Ω(n) time, where n + 1 is the number of stages in the BGSEN. This paper attempts to propose an explicit formula for computing the control tag for routing in the backward network. We will demonstrate how this formula greatly simplifies the computation process and how it leads to efficient routing algorithms. In particular, an O(1)-time one-to-one routing algorithm and an efficient routing-table construction algorithm have been proposed.

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Cao, Suzhen, Junjian Yan, Zixuan Fang, and Caifen Wang. "A Searchable Encryption with Forward/Backward Security and Constant Storage." Applied Sciences 13, no. 4 (February 8, 2023): 2181. http://dx.doi.org/10.3390/app13042181.

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Dynamic searchable encryption satisfies users’ needs for ciphertext retrieval on semi-trusted servers, while allowing users to update server-side data. However, cloud servers with dynamically updatable data are vulnerable to information abuse and file injection attacks, and current public key-based dynamic searchable encryption algorithms are often complicated in construction and high in computational overhead, which is not efficient for practical applications. In addition, the client’s storage costs grow linearly with the number of keywords in the database, creating a new bottleneck when the size of the keyword set is large. To solve the above problems, a dynamic searchable encryption scheme that uses a double-layer structure, while satisfying forward and backward security, is proposed. The double-layer structure maintains a constant client-side storage cost while guaranteeing forward and backward security and further reduces the algorithm overhead by avoiding bilinear pairings in the encryption and decryption operations. The analysis results show that the scheme is more advantageous in terms of security and computational efficiency than the existing dynamic searchable encryption scheme under the public key cryptosystem. It is also suitable for the big data communication environment.

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Li, Chang Bin, Hua Li, and Peng Wei Wang. "Forward Predicting and Backward Verifying Tags Number Estimation in RFID System." Applied Mechanics and Materials 321-324 (June 2013): 2902–5. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2902.

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Tag collision happens when multiple tags are energized simultaneously, reflect their respective signals back to the reader at the same time, and the reader is unable to differentiate these signals. This problem causes decrease of the RFID efficiency especially in the tag-intensive RFID system. The dynamic frame slotted aloha (DFSA) algorithm is the most appropriate solution for this problem, which is based on an accurate tags number estimate method. A novel forward predicting and backward verifying adaptive precise tags number estimation algorithm is proposed in this paper. Experiment results show that it is more effective than others.

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Memon, Sufyan Ali, Ihsan Ullah, Uzair Khan, and Taek Lyul Song. "Smoothing Linear Multi-Target Tracking Using Integrated Track Splitting Filter." Remote Sensing 14, no. 5 (March 6, 2022): 1289. http://dx.doi.org/10.3390/rs14051289.

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Multi-target tracking (MTT) is a challenging issue due to an unknown number of real targets, motion uncertainties, and coalescence behavior of sensor (such as radar) measurements. The conventional MTT systems deal with intractable computational complexities because they enumerate all feasible joint measurement-to-track association hypotheses and recursively calculate the a posteriori probabilities of each of these joint hypotheses. Therefore, the state-of-art MTT system demands bypassing the entire joint data association procedure. This research work utilizes linear multi-target (LM) tracking to treat feasible target detections followed by neighbored tracks as clutters. The LM integrated track splitting (LMITS) algorithm was developed without a smoothing application that produces substantial estimation errors. Smoothing refines the state estimation in order to reduce estimation errors for an efficient MTT. Therefore, we propose a novel Fixed Interval Smoothing LMITS (FIsLMITS) algorithm in the existing LMITS algorithm framework to improve MTT performance. This algorithm initializes forward and backward tracks employing LMITS separately using measurements collected from the sensor in each scan. The forward track recursion starts after the smoothing. Therefore, each forward track acquires backward multi-tracks that arrived from upcoming scans (future scans) while simultaneously associating them in a forward track for fusion and smoothing. Thus, forward tracks become more reliable for multi-target state estimation in difficult cluttered environments. Monte Carlo simulations are carried out to demonstrate FIsLMITS with improved state estimation accuracy and false track discrimination (FTD) in comparison to the existing MTT algorithms.

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Kankam, Kunrada, Watcharaporn Cholamjiak, and Prasit Cholamjiak. "Convergence Analysis of a Modified Forward-Backward Splitting Algorithm for Minimization and Application to Image Recovery." Computational and Mathematical Methods 2022 (October 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/3455998.

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Many applications in applied sciences and engineering can be considered as the convex minimization problem with the sum of two functions. One of the most popular techniques to solve this problem is the forward-backward algorithm. In this work, we aim to present a new version of splitting algorithms by adapting with Tseng’s extragradient method and using the linesearch technique with inertial conditions. We obtain its convergence result under mild assumptions. Moreover, as applications, we provide numerical experiments to solve image recovery problem. We also compare our algorithm and demonstrate the efficiency to some known algorithms.

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Gong, Jian, Yiduo Guo, and Qun Wan. "An Improved Spatial Difference Smoothing Method Based on Multistage Wiener Filtering." Mathematical Problems in Engineering 2019 (November 7, 2019): 1–8. http://dx.doi.org/10.1155/2019/8515606.

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In order to solve the angle estimation problem of coherent sources in the colored background noise, an improved forward and backward spatial difference smoothing algorithm is proposed by combining the improved spatial smoothing algorithm with the spatial difference algorithm. By the algorithm we can not only decoherent the coherent source but also suppress the influence of the color noise. In order to further reduce the computational complexity of the IFBSDS algorithm, an improved forward and backward spatial difference smoothing algorithm based on Wiener filtering is also proposed. Thus, the eigenvalue decomposition operation of subspace class algorithm can be avoided, and at the same time, the same performance with the IFBSDS algorithm can be obtained, which is more consistent with the real demand of MIMO radar signal real-time processing.

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Prohl, Andreas, and Yanqing Wang. "Strong rates of convergence for a space-time discretization of the backward stochastic heat equation, and of a linear-quadratic control problem for the stochastic heat equation." ESAIM: Control, Optimisation and Calculus of Variations 27 (2021): 54. http://dx.doi.org/10.1051/cocv/2021052.

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We verify strong rates of convergence for a time-implicit, finite-element based space-time discretization of the backward stochastic heat equation, and the forward-backward stochastic heat equation from stochastic optimal control. The fully discrete version of the forward-backward stochastic heat equation is then used within a gradient descent algorithm to approximately solve the linear-quadratic control problem for the stochastic heat equation driven by additive noise. This work is thus giving a theoretical foundation for the computational findings in Dunst and Prohl, SIAM J. Sci. Comput. 38 (2016) A2725–A2755.

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Jannati, Mohammad, Nik Rumzi Nik Idris, Mohd Junaidi Abdul Aziz, Tole Sutikno, and M. Ghanbari. "Switching FOC Method for Vector Control of Single-Phase Induction Motor Drives." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 474. http://dx.doi.org/10.11591/ijece.v6i2.9146.

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This paper proposes a novel vector control method based on Rotor flux Field-Oriented Control (RFOC) for single-phase Induction Motor (IM) drives. It is shown that in a rotating reference frame, the single-phase IM equations can be separated into forward and backward equations with balanced structures. In order to accommodate for these forward and backward equations, a drive system consisting of two RFOCs that are switched interchangeably, is proposed. Alternatively, these two RFOC algorithms can be simplified as a single FOC algorithm. The analysis, controller design and simulation of the proposed technique showed that it is feasible for single-phase IM drive for high performance applications.

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Jannati, Mohammad, Nik Rumzi Nik Idris, Mohd Junaidi Abdul Aziz, Tole Sutikno, and M. Ghanbari. "Switching FOC Method for Vector Control of Single-Phase Induction Motor Drives." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 474. http://dx.doi.org/10.11591/ijece.v6i2.pp474-483.

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This paper proposes a novel vector control method based on Rotor flux Field-Oriented Control (RFOC) for single-phase Induction Motor (IM) drives. It is shown that in a rotating reference frame, the single-phase IM equations can be separated into forward and backward equations with balanced structures. In order to accommodate for these forward and backward equations, a drive system consisting of two RFOCs that are switched interchangeably, is proposed. Alternatively, these two RFOC algorithms can be simplified as a single FOC algorithm. The analysis, controller design and simulation of the proposed technique showed that it is feasible for single-phase IM drive for high performance applications.

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35

Wang, Minghao, Juliang Cao, Shaokun Cai, Meiping Wu, Kaidong Zhang, and Ruihang Yu. "Improving the Strapdown Airborne Vector Gravimetry by a Backward Inertial Navigation Algorithm." Sensors 18, no. 12 (December 14, 2018): 4432. http://dx.doi.org/10.3390/s18124432.

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Strapdown airborne gravimetry is an efficient way to obtain gravity field data. A new method has been developed to improve the accuracy of airborne vector gravimetry. The method introduces a backward strapdown navigation algorithm into the strapdown gravimetry, which is the reverse process of forward algorithm. Compared with the forward algorithm, the backward algorithm has the same performance in the condition of no sensor error, but has different error characteristics in actual conditions. The differences of the two algorithms in the strapdown gravimetry data processing are presented by simulations, which show that the two algorithms have different performance in the horizontal attitude measurement and convergence of integrated navigation filter. On the basis of detailed analysis, the procedures of accuracy improvement method are presented. The result of this method is very promising when applying to an actual flight test carried out by a SGA-WZ02 strapdown gravimeter. After applying the proposed method, the repeatability of two gravity disturbance horizontal components were 1.83 mGal and 1.80 mGal under the resolution of 6 km, which validate the effectiveness of the method. Furthermore, the wavenumber correlation filter is also discussed as an alternative data fusion method.

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ARTSAWANG, NATTHAPHON, and KASAMSUK UNGCHITTRAKOOL. "A new forward-backward penalty scheme and its convergence for solving monotone inclusion problems." Carpathian Journal of Mathematics 35, no. 3 (2019): 349–63. http://dx.doi.org/10.37193/cjm.2019.03.09.

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The purposes of this paper are to establish an alternative forward-backward method with penalization terms called new forward-backward penalty method (NFBP) and to investigate the convergence behavior of the new method via numerical experiment. It was proved that the proposed method (NFBP) converges in norm to a zero point of the monotone inclusion problem involving the sum of a maximally monotone operator and the normal cone of the set of zeros of another maximally monotone operator. Under the observation of some appropriate choices for the available properties of the considered functions and scalars, we can generate a suitable method that weakly ergodic converges to a solution of the monotone inclusion problem. Further, we also provide a numerical example to compare the new forward-backward penalty method with the algorithm introduced by Attouch [Attouch, H., Czarnecki, M.-O. and Peypouquet, J., Coupling forward-backward with penalty schemes and parallel splitting for constrained variational inequalities, SIAM J. Optim., 21 (2011), 1251-1274].

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YIN, TZU-CHIEN, NAWAB HUSSAIN, and ASIM ASIRI. "A self-adaptive forward-backward-forward algorithm for solving split variational inequalities." Carpathian Journal of Mathematics 39, no. 2 (December 21, 2022): 553–67. http://dx.doi.org/10.37193/cjm.2023.02.15.

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In this paper, we consider an iterative approximation problem of split variational inequalities in Hilbert spaces. In order to solve this split problem, we construct an iterative algorithm which combines a forward-backward-forward method and a self-adaptive rule to update the step-sizes. We prove that the constructed algorithm converges strongly to a solution of the split variational inequalities under some mild assumptions.

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Lee, Joonwoo, and Won Kim. "Heterogeneous Cooperative Bare-Bones Particle Swarm Optimization with Jump for High-Dimensional Problems." Electronics 9, no. 9 (September 21, 2020): 1539. http://dx.doi.org/10.3390/electronics9091539.

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This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.

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Yang, Zhen-Ping, Yuliang Wang, and Gui-Hua Lin. "Variance-Based Modified Backward-Forward Algorithm with Line Search for Stochastic Variational Inequality Problems and Its Applications." Asia-Pacific Journal of Operational Research 37, no. 03 (April 29, 2020): 2050011. http://dx.doi.org/10.1142/s0217595920500116.

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We propose a variance-based modified backward-forward algorithm with a stochastic approximation version of Armijo’s line search, which is robust with respect to an unknown Lipschitz constant, for solving a class of stochastic variational inequality problems. A salient feature of the proposed algorithm is to compute only one projection and two independent queries of a stochastic oracle at each iteration. We analyze the proposed algorithm for its asymptotic convergence, sublinear convergence rate in terms of the mean natural residual function, and optimal oracle complexity under moderate conditions. We also discuss the linear convergence rate with finite computational budget for the proposed algorithm without strong monotonicity. Preliminary numerical experiments indicate that the proposed algorithm is competitive with some existing algorithms. Furthermore, we consider an application in dealing with an equilibrium problem in stochastic natural gas trading market.

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BUSSABAN, LIMPAPAT, ATTAPOL KAEWKHAO, and SUTHEP SUANTAI. "A parallel inertial S-iteration forward-backward algorithm for regression and classification problems." Carpathian Journal of Mathematics 36, no. 1 (March 1, 2020): 35–44. http://dx.doi.org/10.37193/cjm.2020.01.04.

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In this paper, a novel algorithm, called parallel inertial S-iteration forward-backward algorithm (PISFBA) isproposed for finding a common fixed point of a countable family of nonexpansive mappings and convergencebehavior of PISFBA is analyzed and discussed. As applications, we apply PISFBA to estimate the weight con-necting the hidden layer and output layer in a regularized extreme learning machine. Finally, the proposedlearning algorithm is applied to solve regression and data classification problems

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SHIH, FRANK Y., YI-TA WU, and BRIAN L. C. CHEN. "FORWARD AND BACKWARD CHAIN-CODE REPRESENTATION FOR MOTION PLANNING OF CARS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 08 (December 2004): 1437–51. http://dx.doi.org/10.1142/s0218001404003770.

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The path-planning problem is presented to show a car of any desired shape moving from a starting position to a destination in a finite space with arbitrarily shaped obstacles in it. In this paper, a new chain-code representation is developed to record the motion path when forward and backward movements are allowed. By placing the smooth turning-angle constraint, we can obtain more realistic results to the actual motion of cars. Meanwhile, by combining rotational mathematical morphology and distance transformation, we can obtain the shortest collision-free path. As soon as the distance map and the collision-free codes have been established offline, the shortest paths of cars starting from any location toward the destination can be promptly obtained online. Experimental results show that our algorithm works successfully in different conditions. We also extend our algorithm to the automated parallel parking and three-dimensional path planning.

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Liu, Yuan Min, and Lian Fang Tian. "An Improved Algorithm on Adaptive KLT Vision Tracking." Advanced Materials Research 631-632 (January 2013): 1270–75. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1270.

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In view of the shortage of the KLT (Kanade-Lucas-Tomasi) tracking algorithm, an improved adaptive tracking method based on KLT is proposed in this paper, in which a kind of filtering mechanism is applied to decrease the effects of noise and illumination on tracking system. In order to eliminate the error of tracking, the strategies based on forward-backward error and measurement validity are utilized properly. However, because the approach to forward-backward error makes the feature points reduce, which leads to tracking failure especially when the shapes of object change, a method for appending the feature points is introduced. Experimental results indicate that the method presented in this paper is state of the art robustness in our comparison with related work and demonstrate improved generalization over the conventional methods.

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Jiang, Bernard C., Wen-Hung Yang, and Chi-Yu Yang. "An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification." Industrial Engineering and Management Systems 12, no. 4 (December 31, 2013): 380–88. http://dx.doi.org/10.7232/iems.2013.12.4.380.

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Whitehouse, Gary E., and Gail W. DePuy. "Solving Constrained Multiple Resource Networks Both Forward and Backward Using Brooks Algorithm." Project Management Journal 32, no. 4 (December 2001): 24–31. http://dx.doi.org/10.1177/875697280103200404.

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Bon, Abdul Talib, and Nuhu Isah. "Hidden Markov Model and Forward-Backward Algorithm in Crude Oil Price Forecasting." IOP Conference Series: Materials Science and Engineering 160 (November 2016): 012067. http://dx.doi.org/10.1088/1757-899x/160/1/012067.

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Kyungseok Kim and S. R. Saunders. "New adaptive beamforming algorithm employing forward/backward averaging and signal enhancement schemes." IEEE Communications Letters 5, no. 3 (March 2001): 98–100. http://dx.doi.org/10.1109/4234.913152.

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Huang, Yuanyuan, and Yunda Dong. "New properties of forward–backward splitting and a practical proximal-descent algorithm." Applied Mathematics and Computation 237 (June 2014): 60–68. http://dx.doi.org/10.1016/j.amc.2014.03.062.

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Khodadadian, Amirreza, Maryam Parvizi, Mohammad Teshehlab, and Clemens Heitzinger. "Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks." Sensors 22, no. 13 (June 24, 2022): 4785. http://dx.doi.org/10.3390/s22134785.

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Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.

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Chumpungam, Dawan, Panitarn Sarnmeta, and Suthep Suantai. "An Accelerated Convex Optimization Algorithm with Line Search and Applications in Machine Learning." Mathematics 10, no. 9 (April 30, 2022): 1491. http://dx.doi.org/10.3390/math10091491.

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In this paper, we introduce a new line search technique, then employ it to construct a novel accelerated forward–backward algorithm for solving convex minimization problems of the form of the summation of two convex functions in which one of these functions is smooth in a real Hilbert space. We establish a weak convergence to a solution of the proposed algorithm without the Lipschitz assumption on the gradient of the objective function. Furthermore, we analyze its performance by applying the proposed algorithm to solving classification problems on various data sets and compare with other line search algorithms. Based on the experiments, the proposed algorithm performs better than other line search algorithms.

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Kumar, Abhimanyu, Abhishek Kumar, Rammohan Mallipeddi, and Dong-Gyu Lee. "Adaptive Backward/Forward Sweep for Solving Power Flow of Islanded Microgrids." Energies 15, no. 24 (December 9, 2022): 9348. http://dx.doi.org/10.3390/en15249348.

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This paper presents an algorithm for solving the power flow (PF) problem of droop-regulated AC microgrids (DRACMs) operating in isolated mode. These systems are based on radial distribution networks without having a slack bus to facilitate conventional computations. Moreover, distributed generation units have to distribute the power and voltage regulation among themselves as a function of operating frequency and voltage droop rather than having a slack bus that regulates voltage and power demands. Based on the conventional backward/forward sweep algorithm (BFS), the proposed method is a derivative-free PF algorithm. To manage the absence of a slack bus in the system, the BFS algorithm introduces new loops, equations, and self-adaptation procedures to its computation procedures. A comparison is presented between the proposed BFS algorithm and other state-of-the-art PF algorithms, as well as PSCAD/EMTDC. In comparison to existing algorithms, the proposed approach is fast, simple, accurate, and easy to implement, and it can be considered an effective tool for planning and analyzing islanded DRACMs.

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Journal articles on the topic 'Forward-And-Backward algorithm'

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