Predicting House Price With a Memristor-Based Artificial Neural Network

Wang, J. J.; Hu, Shaoxiang; Zhan, X. T.; Luo, Qiuli; Yu, Qin; Liu, Zhen; Chen, T. P.; Yin, You; Hosaka, Sumio; Liu, Y.

doi:10.1109/access.2018.2814065

Cited by 42 publications

(26 citation statements)

References 17 publications

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“…MNNLs Models: We consider the following experimental MNNLs models (18) and (19) with initial parameters as the drive system and response system, respectivelẏ…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…where i = 1, 2, and On the basis of the MNNLs (18) and (19), two simulation experiments are conducted to verify the effectiveness of the proposed methods, and the specific experiments are described as follows.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…There are many achievements about the design of MNNs [7]- [12] to simulate the behaviors of human brain well, and multiple types of time delays and perturbations are often introduced into the MNNs models, which extend their applications for image chaotic encryption [13]- [15], secure communications [16]- [18] and price prediction [19]. However, few MNNs models involve the problem of multiple connections between neuron nodes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

Wang¹,

Li²,

Peng³

et al. 2020

IEEE Access

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The finite-time lag synchronization problem of memristive neural networks with multi-links (MNNLs) is discussed with the adaptive controllers, and the discrete and distributed time-varying delays are included in the network models. With the Lyapunov functional method and the properties of inequality, two different finite-time methods are adopted to prove and compare the effects of the two proposed theorems, and three corollaries are obtained and to be analyzed. Using the assumptions of Lipschitz continuous and boundedness of the activation functions, three lemmas are derived to deal with the difficulty caused by the memristive jumping properties and synchronization lag feature, which make the proof of the theorems simple and clear. Based on that whether time-varying delays are included, with the adaptive control strategy, we design delay-independent and delay-dependent adaptive controllers with different adaptive parameters for the MNNLs respectively. Finally, two numerical simulations are given to show the effectiveness and correctness of the obtained theoretical results. INDEX TERMS Memristive neural networks with multi-links, finite-time lag synchronization, delaydependent adaptive control, delay-independent adaptive control, distributed time-varying delays.

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“…MNNLs Models: We consider the following experimental MNNLs models (18) and (19) with initial parameters as the drive system and response system, respectivelẏ…”

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

Wang¹,

Li²,

Peng³

et al. 2020

IEEE Access

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show abstract

“…The Davies-Bouldin Index is used to evaluate cluster results by measuring the ratio of the spread of clusters and the distance between clusters. The Silhouette coefficient will be shown at (5), while the Davies-Bouldin Index will be shown at (6) to (8).…”

Section: The Measurement Of Cluster Validitymentioning

confidence: 99%

“…Based on many considered features in determining house prices, the housing data are classified as a high-dimensional data. In some previous studies, Neural Network can be used to predict the price of a house [4][5] [6] [7] [8]. Several approaches of regression techniques to predict the house prices also done by [9][10] which using the time-series data.…”

Section: Introductionmentioning

confidence: 99%

High Dimensional Data Clustering using Self-Organized Map

2019

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As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.

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Mixing Deep Visual and Textual Features for Image Regression

Zhang

2020

Advances in Intelligent Systems and Computing

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Predicting House Price With a Memristor-Based Artificial Neural Network

Cited by 42 publications

References 17 publications

Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

Finite-Time Lag Synchronization of Memristive Neural Networks With Multi-Links via Adaptive Control

High Dimensional Data Clustering using Self-Organized Map

Mixing Deep Visual and Textual Features for Image Regression

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