Taylor Series Prediction of Time Series Data with Error Propagated by Artificial Neural Network

Mangai, S. Alamelu; Sankar, B. Ravi; Alagarsamy, K.

doi:10.5120/15470-4112

Cited by 35 publications

(42 citation statements)

References 9 publications

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“…In this research, a new model is developed to find the future updates of the position. Based on this perspective, the Taylor series model and its predictive theory 40 are integrated with the SMO algorithm 14 to find the position of the next iteration. The Taylor series is used to get more theoretical error bounds and it is very useful for the derivations.…”

Section: Proposed Taysmo Algorithmmentioning

confidence: 99%

“…In the model, Taylor series 40 is defined as the representation of function, where the infinite sum terms are computed through the values of function derivatives that exist at single point. When the Taylor series is focused toward the center location at zero then, the Taylor series is named as Maclaurin series.…”

Section: Proposed Taysmo Algorithmmentioning

confidence: 99%

“…However, in this research, the Taylor series update equation consists of eight coefficients, 40 and the Taylor series updated equation is expressed as,

\begin{array}{l} P ((), k + 1) & = 0.5 P ((), k) + 1.3591 P ((), k - 1) - 1.3590 P ((), k - 2) + 0.6795 P ((), K - 3) - 0.2295 ((), k - 4) \\ + 0.0555 P ((), k - 5) - 0.010 P ((), k - 6) + 1.38 e - 3 P ((), k - 7) - 9.92 e - 5 P ((), k - 8) \end{array}

…”

Section: Proposed Taysmo Algorithmmentioning

confidence: 99%

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Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Menon

Kulkarni

Singh

et al. 2021

Numerical Meth Engineering

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Multi‐objective optimization is used for optimizing a number of objectives simultaneously. Mostly, the optimization algorithms considered the previous iterative position to find the next position updates. The main intention of this research is to design and develop a new model to solve the computational complexity, and the resource allocation problem. Based on this perspective, the Taylor series model and its predictive theory are applied to Spider Monkey Optimization (SMO), and a new optimization, named Taylor‐Spider Monkey Optimization (TaySMO) is developed. The proposed TaySMO computes the updated position of the swarm using the local leader phase and the global leader phase. However, a new position update equation is derived to enhance the searching process of the SMO. Here, multiple objectives such as, throughput, power, and fairness index are considered to solve the resource allocation problem. However, the performance of the proposed algorithm is evaluated using the conventional optimization function in terms of fitness function and convergence criteria as the mean square error (MSE) with the neural network learning is 0.3747, congestion rate of the resource allocation problem is 8.736E‐23, and MSE of the spectrum sensing is 8.74E‐23, respectively.

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Section: Proposed Taysmo Algorithmmentioning

confidence: 99%

Section: Proposed Taysmo Algorithmmentioning

confidence: 99%

“…However, in this research, the Taylor series update equation consists of eight coefficients, 40 and the Taylor series updated equation is expressed as,

\begin{array}{l} P ((), k + 1) & = 0.5 P ((), k) + 1.3591 P ((), k - 1) - 1.3590 P ((), k - 2) + 0.6795 P ((), K - 3) - 0.2295 ((), k - 4) \\ + 0.0555 P ((), k - 5) - 0.010 P ((), k - 6) + 1.38 e - 3 P ((), k - 7) - 9.92 e - 5 P ((), k - 8) \end{array}

…”

Section: Proposed Taysmo Algorithmmentioning

confidence: 99%

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Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Menon

Kulkarni

Singh

et al. 2021

Numerical Meth Engineering

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“…Applying the hyperbolic tangential function to the Taylor series improves the robustness of the clustering. (3) Update the cluster centers : The cluster centers are updated following the update in the membership degree that is given as,

A_{i} ((), t + 1) = \frac{\sum_{b = 1}^{d} M_{bi}^{v} κ ((),, ω_{b}, A_{i}) . ω_{b}}{\sum_{b = 1}^{d} M_{bi}^{v} κ ((),, ω_{b}, A_{i})} .

The Equation (11) is the cluster center update equation using the standard KFCM clustering algorithm, which is modified using the Taylor series that is enumerated as follows. The Taylor series 57 is given as,

\begin{matrix} lefttrue \\ A_{i} (t + 1) = 0.5 A_{i} (t) + 1.3591 A_{i} (t - 1) - 1.359 A_{i} (t - 2) + 0.6795 A_{i} (t - 3) - 0.2259 A_{i} (t - 4) \\ + 0.055 A_{i} (t - 5) - 0.0104 A_{i} (t - 6) + 1.38 e^{- 5} A_{i} (t - 7) - 9.92 e^{- 5} A_{i} (t - 8) \end{matrix}

…”

Section: Proposed Multihop Routing Protocol For the Wsn With The Rssamentioning

confidence: 99%

A modified rider optimization algorithm for multihop routing in WSN

Augustine¹,

Ananth²

2020

Int J Numerical Modelling

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Wireless Sensor Networks (WSNs) have an important role in establishing the communication between the Internet of things (IoT) devices. Routing is an important research field in the WSN as it helps in finding the suitable paths for the communication. This paper proposes a multipath routing algorithm based on the optimization approach. The proposed routing algorithm has two important phases, namely, Cluster head selection and multipath routing. Initially, the cluster head selection is carried out by the kernel based Fuzzy C Means (kernel FCM) algorithm. Then, the multipath routing is established by the newly developed Rider Salp Swarm Optimization algorithm (RSSA), which is the integration of the Rider Optimization Algorithm (ROA) and Salp Swarm Algorithm (SSA). The fitness function of the proposed RSSA is designed by considering the several factors, such as energy, QoS and trust. The simulation of the proposed work is carried out using different WSN setups, and the results are compared with several comparative techniques. From the results, it can be summarized that the proposed RSSA based multipath routing has better performance with values of 0.2526, 0.0764, 0.5, and 26 for delay, energy, throughput and number of alive nodes, respectively.

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“…This research introduces the Taylor‐BLMS algorithm for adjusting the weights present in the beamforming model. The proposed Taylor‐BLMS models make use of the Taylor series model for adjusting the weights of the beamforming model in both the forward and backward directions. The Taylor series model predicts the weights similar to the autoregressive model.…”

Section: Proposed Taylor Integrated Bidirectional Least Mean Square Amentioning

confidence: 99%

Multipath mitigation in GPS receiver using Taylor integrated bidirectional least mean square algorithm

Siridhara

Ratnam

2019

Trans Emerging Tel Tech

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Research interest in Global Navigation Satellite System (GNSS) is increasing in the past few years due to its wide applicability in many application-oriented environments, such as automobile navigation system and aircraft landing system. Global Positioning System (GPS) is one of the GNSSs having high precision navigation performance. Multipath is a dominant error source that occurs in several GPS applications, limiting the performance. To reduce the multipath effects of satellite signals, an effective multipath mitigation technique is required. This paper introduces the Taylor series-based bidirectional least mean square (Taylor-BLMS) beamforming model for the multipath mitigation problems occurring in the GPS receiver. The performance metrics, such as antenna gain and bit error rate (BER), measure the performance of the proposed Taylor-BLMS model against various comparative models. For the system with six sensors, the proposed Taylor-BLMS model has the best performance with the values of 23.460 dB and 4.78E−05 for the antenna gain and the BER, respectively. For the system with 1 jammer, the proposed Taylor-BLMS model has the antenna gain and the BER values of 22.2828 dB and 3.69E−05, respectively. Trans Emerging Tel Tech. 2019;30:e3760. wileyonlinelibrary.com/journal/ett

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Taylor Series Prediction of Time Series Data with Error Propagated by Artificial Neural Network

Cited by 35 publications

References 9 publications

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

A modified rider optimization algorithm for multihop routing in WSN

Multipath mitigation in GPS receiver using Taylor integrated bidirectional least mean square algorithm

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