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Help
If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.
Customer service
All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com
Sale & Press
If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com
La forma clsica de abordar el problema de ajuste ptimo de un conjunto de pares de datos [(x1, y1), (x2, y2). (xn1, yn1),( xn, yn)] consiste en fijar una funcin concreta, que dependa de ciertos parmetros, y despus realizar una regresin de los mismos por el mtodo de mnimos cuadrados. El problema con este mtodo es que se necesita conocer la forma especfica de la funcin que debe ajustar los puntos. Este libro ofrece una herramienta que, por medio de una funcin simple o compuesta (funcin de funcin), cuya estructura pueda definirse libremente, es capaz de explorar tanto el espacio de las funciones como el espacio de los parmetros (coeficientes), de modo que el usuario solo especifique la estructura formal de la funcin de ajuste y la herramienta se encargue del resto. La obra se centra en dar una solucin a la bsqueda de una funcin de ajuste, por el mtodo de regresin, mediante el empleo de algoritmos genticos.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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⚠️ California Proposition 65 Warning: Some products sold on this website may expose you to chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm. For more information, visit www.P65Warnings.ca.gov.
