Heapsort
1.A heap is a binary tree satisfying the followingconditions:
-This tree is completely balanced.
-If the height of this binary tree is h, then leaves can be at level h or level h-1.
-All leaves at level h are as far to the left as possible.
-The data associated with all descendants of a node are smaller than the datum associated with this node.
Implementation
1.using a Linear array not a binary tree.
-The sons of A(h) are A(2h) and A(2h+1).
2.time complexity: O(n log n)
DEMO_COND demonstrates the role of the condition
number of a matrix (with respect to inversion)
in the role of Linear system solving.
Matthias Heinkenschloss
Department of Computational and Applied Mathematics
Rice University
Feb 22, 2001
NN Functions
a program in Lisp to demonstrate working of an artificial neuron. (Enter an input vector X and weight vector W. Calculate weighted sum XW. Transform this using signal or activation functions like logistic, threshold, hyperbolic-tangent, Linear, exponential, sigmoid or some other functions (syntax provided) and display the output).
In this paper, the feasibility of replacing a chaos source by an equivalent digital pseudo-random generator realized using Linear Feedback Shift Register (LFSR) is studied. Particular emphasis is given on the digital implementation Piece-Wise Linear A ffine Maps (PWAM). As an application, an FPGA implementation of four different maps has been experimentally verifi ed in a FM-DCSK test radio system.
Abstract—Stable direct and indirect decentralized adaptive radial basis
neural network controllers are presented for a class of interconnected
nonLinear systems. The feedback and adaptation mechanisms for each
subsystem depend only upon local measurements to provide asymptotic
tracking of a reference trajectory. Due to the functional approximation
capabilities of radial basis neural networks, the dynamics for each
subsystem are not required to be Linear in a set of unknown coeffi cients
as is typically required in decentralized adaptive schemes. In addition,
each subsystem is able to adaptively compensate for disturbances and
interconnections with unknown bounds.
Topics Practices:
Programming and Numerical Methods
Practice 1: Introduction to C
Practice 2: Cycles and functions
First part cycles
Part Two: Roles
Practice 3 - Floating point arithmetic
Practice 4 - Search for roots of functions
Practice 5 - Numerical Integration
Practice 6 - Arrangements and matrices
Part One: Arrangements
Part II: Matrices
Practice 7 - Systems of Linear equations
Practice 8 - Interpolation
Practice 9 - Algorithm Design Techniques
