Lecture Notes Linear Differential Equations 4664 Introduction Falling Cat 1 st

# Lecture notes linear differential equations 4664

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Lecture Notes – Linear Differential Equations — (46/64) Introduction Falling Cat 1 st Order Linear DEs Examples Pollution in a Lake Example 2 Mercury in Fish Modeling Mercury in Fish Least Squares Error with MatLab Define a MatLab function for the sum of square errors between the data and the model L ( t ; L * , b ) = L * ( 1 - e - bt ) 1 function J = sumsq vonBert (p , tdata , ldata ) 2 % Function computing sum of square e r r o r s f o r von B e r t a l a n f f y model 3 model = p (1) * (1 - exp ( - p (2) * tdata ) ) ; 4 e r r o r = model - ldata ; 5 J = e r r o r * error ’ ; 6 end If the data are stored in tdfish and ldfish , then apply the MatLab function fminsearch with function sumsq vonBert 1 [ p1 , J , f l a g ] = fminsearch ( @sumsq vonBert , [ 1 0 0 , 0 . 1 ] , [ ] , t d f i s h , l d f i s h ) The result are the best fitting parameters L * = 92 . 401 and b = 0 . 14553 Joseph M. Mahaffy, h [email protected] i Lecture Notes – Linear Differential Equations — (47/64) Introduction Falling Cat 1 st Order Linear DEs Examples Pollution in a Lake Example 2 Mercury in Fish Modeling Mercury in Fish Graph of Length of Lake Trout Graph for Length of Lake Trout: Shows data and best fitting von Bertalanffy model 0 2 4 6 8 10 12 14 16 18 20 0 10 20 30 40 50 60 70 80 90 100 Age (Years) Length (cm) Length of Lake Trout Joseph M. Mahaffy, h [email protected] i Lecture Notes – Linear Differential Equations — (48/64)

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Introduction Falling Cat 1 st Order Linear DEs Examples Pollution in a Lake Example 2 Mercury in Fish Modeling Mercury in Fish Lake Trout Data for Weight vs Length Lake Trout Data for Weight vs Length (Lake Superior, 1997) length Weight length Weight length Weight length Weight (cm) (g) (cm) (g) (cm) (g) (cm) (g) 29.0 200 57.7 1520 69.6 2800 78.5 3500 51.8 1600 58.9 1600 71.4 3050 80.3 4500 54.9 1450 58.9 1800 75.2 3920 83.8 5000 55.4 1300 60.2 2200 75.4 3980 85.6 4350 56.6 1350 62.5 1800 76.5 3980 86.6 4500 56.6 1660 68.1 3400 78.5 3629 87.4 4650 57.4 1550 Find the best fitting allometric model W ( L ) = kL a for some parameters k and a Joseph M. Mahaffy, h [email protected] i Lecture Notes – Linear Differential Equations — (49/64) Introduction Falling Cat 1 st Order Linear DEs Examples Pollution in a Lake Example 2 Mercury in Fish Modeling Mercury in Fish Allometric Models 1 Allometric Models: Relationship between Length and Weight of Lake Trout using a Power Law Relationship W ( L ) = kL a Examine 3 versions of the Allometric or Power Law model Best fit through the logarithms of the data Nonlinear least squares best fit Dimensional analysis modeling This is algebraic and not a differential equation Dimensional considerations important in differential equations Show a variety of MatLab programming methods Joseph M. Mahaffy, h [email protected] i Lecture Notes – Linear Differential Equations — (50/64) Introduction Falling Cat 1 st Order Linear DEs Examples Pollution in a Lake Example 2 Mercury in Fish Modeling Mercury in Fish Allometric Models 2 Logarithm of Allometric Model: W ( L ) = kL a ln( W ) = a ln( L ) + ln( k ) Let y = ln( W ) be the dependent variable and x = ln( L ) be the independent variable With b = ln( k ) this logarithmic form is a linear relation, y = ax + b Easy formulas for finding a and b for data y vs x Take logarithms of the length and weight data, ln( L ) and ln( W ) Use MatLab to find linear least squares fit to these logarithmic data Obtain Allometric model
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