Jan Zijlstra, Math 3120 Fall 2006
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The Use of MAPLE in Models involving Differential Equations.
|
Section A.1 A Learning Model
| Model Hypothesis: | 'The rate of learning is proportional to the amount left to learn' |
| Corresponding DE: | y'(t) = k ( 100 - y(t) ) |
| Symbols: |
y(t): amount mastered, in % of the learning task, at time
t (hours). |
> eq1 := diff(y(t),t)=k*(100-y(t));
> sol1B := rhs(dsolve({eq1, init1}, y(t)));
>
plot(subs(k=0.05, sol1B),t=0 . . 1,color=blue,
title=`solution of the learning model for
k=0.05`);
Section A.2. A Model for Exponential Growth
| Model Hypothesis: | 'The population growth is proportional to the population size ' |
| Corresponding DE: | y'(t) = k y(t) |
| Symbols: |
y(t) : population size, in millions, at time t
(years). |
> sol2B := rhs(dsolve({eq2} union {init2}, y(t)));
Section A.3. A Logistic Growth Model
| Model Hypothesis: | 'The relative population growth is proportional to the difference
between the maximum sustainable population, M ,and the population size' |
| Corresponding DE: | y'(t)/y(t)= k [M - y(t)] or y'(t) = k y(t) [M - y(t)] |
| Symbols: |
y(t) : population size, in millions, at time t
(years) |
> eq3 :=
diff(y(t),t)=k*y(t)*(M-y(t));
sol3A :=
dsolve(eq3,y(t));
>
init3:=y(0)=3;
sol3B := rhs(dsolve({eq3} union {init3},
y(t)));