- Least Mean Square (LMS) algorithm is online learning algorithm developed by Widrow and Hoff in 1960.
- Rosenblatt’s perceptron was the first learning algorithm for solving linearly separable pattern-classification problem.
- LMS algorithm was the first linear adaptive-filtering algorithm for solving problems such as prediction and communication-channel equalization.
Advantages behind LMS Algorithm:
- Computationally Efficient: Its complexity is linear with respect to adjustable parameters.
- Simple to code and easy to build.
- Robust with respect to external disturbances.
- Gauss-Newton Method makes a balance between computational complexity and convergence behavior.
- Diagonal loading concept.
- Stabilizer term concept.
- A stochastic process has the Markov property if the conditional probability distribution of future states of the process depends only upon the present state; that is, given the present, the future does not depend on the past. A process with this property is called Markov process. The term strong Markov property is similar to this, except that the meaning of “present” is defined in terms of a certain type of random variable, which might be specified in terms of the outcomes of the stochastic process itself, known as a stopping time.
- A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with unobserved state. An HMM can be considered as the simplest dynamic Bayesian network
128-In Gauss-Newton Method, Why we’ve get sum over squres of error signal?
- To make the value positive.
- Why not just get the sum of the error itself.
- 128- “Linearize the dependence of e(i) on w”, Is That mean, trying to find a linear function that maps the dependence between e(i) and w?
- 128-How equation 3.18 has linearized e(i) and w?
- 129-Why they call it Jacobean matrix?
- 129-How we’ve calculated equation 3.20
- 129-What’s meant by nonsingular matrix multiplication?
- 129-Last paragraph that’s talking about Jacobean matrix conditions.