Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling

Chen, Sheng; Hong, Xia; Harris, C.J.

doi:10.1109/tevc.2009.2035921

Cited by 66 publications

(5 citation statements)

References 116 publications

(100 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each particle represents an adaptive value assigned by the objective function fitness (·). To obtain the optimal objective values of the regression parameters, the particles’ positions and velocity s are updated with reference to their two current extreme values as follows [26]:Vθ(t)=ωVθ(t−1)+c1r1(Pbestθ−Xθ(t−1))+c2r2(Gbestθ−Xθ(t−1)), Xθ(t)=Xθ(t−1)+Vθ(t). where ω is a negative inertia factor; c1 and c2 are the particle learning rate and global learning rate, respectively; r1 and …”

Section: Improved Methodsmentioning

confidence: 99%

A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

Zhang

Yin

et al. 2019

Sensors

View full text Add to dashboard Cite

The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression.

show abstract

Section: Improved Methodsmentioning

confidence: 99%

A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

Zhang

Yin

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…From the point of view of the computational complexity, the problem of RBFN design belongs to NP-hard class [6], [8], [35], [47].…”

Section: Complexity Of the Rbf Network Designmentioning

confidence: 99%

“…In [8] it was observed that RBFN parameters, including cluster centroids, numbers of nodes as well as output weights, can be trained together via nonlinear optimization. A review of several different approaches for such integrated training is also included in [8]. However, it is underlined that such learning is computationally expensive and may encounter the problem of local minima.…”

Section: Complexity Of the Rbf Network Designmentioning

confidence: 99%

“…The RBFN design requires setting values of different parameters related to the network initialization and output weights estimation. Since designing and training of the RBF neural networks can be seen as a combinatorial optimization problem belonging to the NP-hard class [6], [8] using a metaheuristic algorithm seems appropriate.…”

Section: Agent-based Population Learning Framework For the Rbf Nementioning

confidence: 99%

See 1 more Smart Citation

Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

2021

View full text Add to dashboard Cite

“…With regards to training, there are three major types of approaches for positioning basis prototypes: heuristic [4][5][6][7], unsupervised [8][9][10], and supervised [11][12][13][14][15][16][17] basis function prototype selection strategies. Hybrid approaches [18][19][20][21], which combine elements from unsupervised and supervised strategies, could additionally be employed.…”

mentioning

confidence: 99%

An Exact Reformulation of Feature-Vector-Based Radial-Basis-Function Networks for Graph-Based Observations

Sledge

Prı́ncipe

2020

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Radial-basis-function networks are traditionally defined for sets of vector-based observations. In this short paper, we reformulate such networks so that they can be applied to adjacency-matrix representations of weighted, directed graphs that represent the relationships between object pairs. We restate the sum-of-squares objective function so that it is purely dependent on entries from the adjacency matrix. From this objective function, we derive a gradient descent update for the network weights. We also derive a gradient update that simulates the repositioning of the radial basis prototypes and changes in the radial basis prototype parameters. An important property of our radial basis function networks is that they are guaranteed to yield the same responses as conventional radial-basis networks trained on a corresponding vector realization of the relationships encoded by the adjacency-matrix. Such a vector realization only needs to provably exist for this property to hold, which occurs whenever the relationships correspond to distances from some arbitrary metric applied to a latent set of vectors. We therefore completely avoid needing to actually construct vectorial realizations via multi-dimensional scaling, which ensures that the underlying relationships are totally preserved.Conventional radial-basis-function (RBF) networks have a feed-forward architecture that consists of two layers: a non-linear hidden layer followed by a linear output layer. The hidden-layer processing elements operate on the weighted distance between a vector observation and some other vector, which is referred to as either an RBF prototype or an RBF center. The RBF prototypes specify the position of a local receptive field. The response of each processing element in this network layer is a non-linear, radially-symmetric function of this observation-prototype distance. The hidden-layer responses are then weighted and, usually, linearly combined by at the output layer.These networks, as conceived by Broomhead and Lowe [1], rely on a vector-based paradigm: they are applied solely to feature-based vectorial observations [2]. Here, we provide a reformulation of RBF networks so that they can be applied to adjacency-matrix representations of weighted, directed graphs. The adjacency representations are symmetric, positive, anti-reflexive matrices of relationship-based observations between object pairs. Such types of observations are prevalent in a number of problem domains, as investigators may be unable to extract meaningful features about various observations yet can easily codify the relationships between them. Pertinent examples include assessing shape similarity and quantifying the relationship between gene ontology products.The graph-based RBF networks that we consider have a feed-forward architecture analogous to that of vectorbased RBF networks. That is, the hidden layer non-linearly transforms the weighted relationship-based observations while the output layer weights and linearly combines those transformed results. There are, however,...

show abstract

Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling

Cited by 66 publications

References 116 publications

A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

An Exact Reformulation of Feature-Vector-Based Radial-Basis-Function Networks for Graph-Based Observations

Contact Info

Product

Resources

About