Search Results for author:
Found 11 papers, 0 papers with code
Recursive LMMSE-Based Iterative Soft Interference Cancellation for MIMO Systems to Save Computations and Memories
Firstly, a reordered description is given for the linear minimum mean square error (LMMSE)-based iterative soft interference cancellation (ISIC) detection process for Mutipleinput multiple-output (MIMO) wireless communication systems, which is based on the equivalent channel matrix.
Improved Recursive Algorithms for V-BLAST to Save Computations and Memories
In the existing algorithm with speed advantage, the proposed algorithm I with speed advantage replaces Improvement I with Improvement V, while the proposed algorithm II with both speed advantage and memory saving replaces Improvements I and II with Improvements V and VI, respectively.
Low-Memory Implementations of Ridge Solutions for Broad Learning System with Incremental Learning
However, the existing low-memory BLS implementation sacrifices the testing accuracy as a price for efficient usage of memories, since it can no longer obtain the generalized inverse or ridge solution for the output weights during incremental learning, and it cannot work under the very small ridge parameter that is utilized in the original BLS.
Efficient and Stable Algorithms to Extend Greville's Method to Partitioned Matrices Based on Inverse Cholesky Factorization
Greville's method has been utilized in (Broad Learn-ing System) BLS to propose an effective and efficient incremental learning system without retraining the whole network from the beginning.
Efficient Inverse-Free Incremental and Decremental Algorithms for Multiple Hidden Nodes in Extreme Learning Machine
The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of a Hermitian matrix.
Complexity Comparison between Two Optimal-Ordered SIC MIMO Detectors Based on Matlab Simulations
Based on our shared Matlab code, we compare the computational complexities between the two detectors in [1] and [2] by theoretical complexity calculations and numerical experiments.
Efficient Decremental Learning Algorithms for Broad Learning System
The decremented learning algorithms are required in machine learning, to prune redundant nodes and remove obsolete inline training samples.
Efficient Ridge Solution for the Incremental Broad Learning System on Added Nodes by Inverse Cholesky Factorization of a Partitioned Matrix
The proposed algorithms 1 and 2 can reduce the computational complexity, since usually the Hermitian matrix in the ridge inverse is smaller than the ridge inverse.
Efficient Inverse-Free Algorithms for Extreme Learning Machine Based on the Recursive Matrix Inverse and the Inverse LDL' Factorization
The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of a Hermitian matrix.
Two Efficient Ridge Solutions for the Incremental Broad Learning System on Added Inputs
This paper proposes the recursive and square-root BLS algorithms to improve the original BLS for new added inputs, which utilize the inverse and inverse Cholesky factor of the Hermitian matrix in the ridge inverse, respectively, to update the ridge solution.
Reducing the Computational Complexity of Pseudoinverse for the Incremental Broad Learning System on Added Inputs
Specifically, when q > k, the proposed algorithm computes only a k * k matrix inverse, instead of a q * q matrix inverse in the existing algorithm.
