Measuring Signal Similarities
Measure signal similarities. It will help you answer questions such as: How do I compare signals with different lengths or different sampling rates? How do I find if there is a signal or just noise in a measurement? Are two signals related? How to measure a delay between two signals (and how do I align them)? How do I compare the frequency content of two signals?…
Introduction to MIMO Systems
Multiple-Input-Multiple-Output (MIMO) systems, which use multiple antennas at the transmitter and receiver ends of a wireless communication system. MIMO systems are increasingly being adopted in communication systems for the potential gains in capacity they realize when using multiple antennas. Multiple antennas use the spatial dimension in…
Perform basic peak analysis. It will help you answer questions such as: How do I find peaks in my signal? How do I measure distance between peaks? How do I measure the amplitude of peaks of a signal which is affected by a trend? How do I find peaks in a noisy signal? How do I find local minima?
Portfolio Optimization Examples
The following sequence of examples highlights features of the Portfolio object in the Financial Toolbox™. Specifically, the examples show how to set up mean-variance portfolio optimization problems that focus on the two-fund theorem, the impact of transaction costs and turnover constraints, how to obtain portfolios that maximize the Sharpe ratio,…
Perform classification using discriminant analysis, naive Bayes classifiers, and decision trees. Suppose you have a data set containing observations with measurements on different variables (called predictors) and their known class labels. If you obtain predictor values for new observations, could you determine to which classes those…
Using the Kalman Filter to Estimate and Forecast the Diebold-Li Yield Curve Model
In the aftermath of the financial crisis of 2008, additional solvency regulations have been imposed on many financial firms, placing greater emphasis on the market valuation and accounting of liabilities. Many firms, notably insurance companies and pension funds, write annuity contracts and incur long-term liabilities that call for sophisticated approaches to model and forecast yield curves.
Lagrange Interpolation Polynomial
If you have a set of N points on a cartesian plane, there will always exist an N-1th order polynomial of the form y = a_0 + a_1.x + a_2.x^2 + ... a_n-1.x^(n-1) which passes through all the points. Lagrange came up with a neat approach to finding this polynomial, which is to construct a set of `basis' polynomials which are zero at all the specified points except for one, then scale and add them to match all the control points. LAGRANGEPOLY returns this polynomi
This product allows users to interactively design a tabular expression. The resusulting function can be saved as a Simulink block or to a Matlab m-file. Tabular Expressions can be proved to be disjoint and complete using the PVS theorem prover. This allows users to ensure that the table they are designing has covered all possible inputs and is deterministic.
Demo file for batchpleas.m
batchpleas is a wrapper for lsqnonlin, allowing it to solve many small problems (all with the same parameterization) in one batched, partitioned nonlinear least squares estimation. This takes advantage of economies of scale, so as to gain a higher throughput overall. The gain can be dramatic.
Computational cost for Cramer's rule
There are plenty of direct and iterative methods to solve a linear algebraic system of equations. Using Cramer's rule, one can easily obtain the solution for small systems by hand. However, with the growth of the unknowns, the method becomes computationally very expensive. Moreover, calculating a determinant by its definition may result in overflow or underflow if someone wanted to apply it on a computer. That is why Cramer's algorithm is not applied in computations.
Modeling an Automatic Transmission Controller
This example shows how to model an automotive drivetrain with Simulink®. Stateflow® enhances the Simulink model with its representation of the transmission control logic. Simulink provides a powerful environment for the modeling and simulation of dynamic systems and processes. In many systems, though, supervisory functions like changing modes or invoking new gain schedules must respond to events that may occur and conditions that develop over time. As a result, the environment requires a language capable of
Designing a Guidance System in MATLAB and Simulink
This example shows how to use the model of the missile airframe presented in a number of published papers (References ,  and ) on the use of advanced control methods applied to missile autopilot design. The model represents a tail controlled missile travelling between Mach 2 and Mach 4, at altitudes ranging between 10,000ft (3,050m) and 60,000ft (18,290m), and with typical angles of attack ranging between +/-20 degrees.