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Basic and advanced electronic fuel injection tuning for Ford vehicles from 1988-2012 using SCT Advantage tuning software.
Computer Calibration of 2011 to 2015 Fords
Greg Banish takes his best-selling title, Engine Management: Advanced Tuning, one step further as he goes in-depth on the combustion basics of fuel injection as well as benefits and limitations of standalone. Learn useful formulas, VE equation and airflow estimation, and more. Also covered are setups and calibration, creating VE tables, creating timing maps, auxiliary output controls, start to finish calibration examples with screen shots to document the process. Useful appendixes include glossary and a special resources guide with standalone manufacturers and test equipment manufacturers
Tuning engines can be a mysterious art, all engines need a precise balance of fuel, air, and timing in order to reach their true performance potential. Engine Management: Advanced Tuning takes engine-tuning techniques to the next level, explaining how the EFI system determines engine operation and how the calibrator can change the controlling parameters to optimize actual engine performance. It is the most advanced book on the market, a must-have for tuners and calibrators and a valuable resource for anyone who wants to make horsepower with a fuel-injected, electronically controlled engine.
Basic and advanced electronic fuel injection tuning for Ford vehicles from 1988-2012 using HP Tuners tuning software.
Textbook covering EFI GM vehicle tuning using HP Tuners software.
Loaded with stunning photography of the entire history of the most iconic muscle car ever produced, this collection will blow away any fan.
Basic and advanced electronic fuel injection tuning for Ford vehicles from 1988-2012 using Delta Force tuning software.
This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.