ExponentialModel
詳細
- ExponentialModelは,記号あるいは数値による評価とフィットに相応しい形式の,指定された変数による指数関数を表す.
- 指数モデルは,成長,減衰,緩和,平衡への接近などのように,変化が現在の状態に比例する過程を記述する.
- 一変量ExponentialModel[]は
としてパラメータ化される. - 多変量指数関数は
としてパラメータ化される. - 指定されない場合,変数は自動的にx[i]を用いて列挙される.
- 有効な変数指定 vars には次のものがある.
-
n 変数の数 symb 単一の変数の記号的表現 {symb1,…} 記号変数のリスト - 指定されていない場合,パラメータは自動的にC[i]を用いて列挙される.
- 形式{par1,…}による有効なパラメータ pars の指定には以下がある.
-
val 固定されたパラメータ値 val par 記号的なパラメータ名 par parval 固定値 val に設定された記号名 par {par,val0} 初期値 val0を持つ記号的なパラメータ名 par - モデルの特性はInformation[PowerModel[…],prop]を用いて抽出できる.
- 有効な基本特性には以下がある.
-
"BaseType" モデルの基本型 "Name" モデル名 "ShortName" ラベルとして使用する短い識別子 "InputType" サポートされる入力型 "OutputType" サポートされる出力型 - 有効なデータ関連の特性には以下がある.
-
"ColumnNames" 入力特徴の名前 "ColumnVariableMap" 列名とモデル変数との対応付け "InputSize" 入力の次元数 "OutputSize" 出力の次元数 "Trainable" モデルが完全に指定されており学習可能であるかどうか "Trained" モデルが数値的に評価可能であるかどうか "VariableColumnMap" モデル変数と列名との対応付け "Variables" モデル変数の名称 - 最良モデルに関連する特性には以下がある.
-
"Expression" モデル式 "Function" 純関数としてのモデル "SymbolicExpression" 記号パラメータを含むモデル式 "TabularFunction" 表形式の行に対する動作に適した純関数 - パラメータに関連する特性には以下がある.
-
"ParameterAssociation" パラメータ名と値の連想 "ParameterCount" パラメータの数 "ParameterInitialValues" フィットのための初期値 "ParameterNames" パラメータ名 "ParameterRules" パラメータ名と値からなる規則のリスト "Parameters" 存在する場合はパラメータ値,それ以外の場合は名前 "ParameterValues" パラメータ値 "Constraints" パラメータの制約条件
変数
パラメータ
特性
例題
すべて開く すべて閉じる例 (3)
ExponentialModel[]ExponentialModel[{a, b, c}, 1]Plot[{ExponentialModel[{0, 1, 1}, 1][{x}], ExponentialModel[{1, 1, -1}, 1][{x}], ExponentialModel[{-0.5, 1, .5}, 1][{x}]}, {x, -1, 1}]スコープ (22)
変数 (6)
ExponentialModel[]ExponentialModel[][{x}]
ExponentialModel[][{x, y, z}]ModelFitは,変数の数がデータ点の次元よりも1少ないと仮定する:
ModelFit[{{0, 0, 0}, {1, 9, 55}, {1, 1, 7}, {1, 4, 25}, {8, 9, 118}, {8, 10, 124}, {8, 5, 94}}, ExponentialModel[]]ExponentialModel[2]ExponentialModel[{var1, var2}]ExponentialModel[{var1, var2}][{a, b}]Plot3D[Evaluate@ExponentialModel[{0, 1, -0.5, .4}, 2][{x, y}], {x, -1, 1}, {y, -1, 1}, ColorFunction -> "Rainbow"]パラメータ (4)
ExponentialModel[2]model = ExponentialModel[ {c, A, k}, 1]model = ExponentialModel[{0, 2, k}, 1]model = ExponentialModel[{c, a -> 2, k}, 1]順序は,C[n]の自動列挙と一致する:
ExponentialModel[][x]評価 (5)
ExponentialModel[1][x]ExponentialModel[2][{x, y}]ExponentialModel[][x]ExponentialModel[][{x, y}]ExponentialModel[2][{{x, y}, {a, b}}]ExponentialModel[1][{{1}, {2}, {3}}]情報 (5)
Information[ExponentialModel[]]情報の中には変数またはパラメータが完全に指定されていないと取得できないものがある:
Information[ExponentialModel[{x, y}]]Information[ExponentialModel[{a, b, c}, x], "Variables"]Information[ExponentialModel[{a, b, c}, x], {"Variables", "Parameters"}]Information[ExponentialModel[1], {"Variables", "Parameters"}]アプリケーション (9)
基本的なアプリケーション (8)
抵抗を介して充電されるとき,コンデンサの両端の電圧は供給電圧に指数関数的に近付く:
model = ModelFit[data -> "Voltage", ExponentialModel[]]model = ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Voltage" -> Association["ElementType" -> "Real64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
{{TabularColumn[Association["Data" -> {{5.838988328758781, 6.738191996308977,
6.19608830911937, 5.80789503296751, 6.250604171731046, 5.179844714242305,
8.721810088941064, 1.7205702996647743, 8.577864810423153, 0.7222558265533063,
4.338023011638031, 6.7412711375946515, 1.9644724536929292, 5.046552471939199,
0.9969698240844305, 4.337188925837601, 7.020937526546442, 8.335043162393749,
4.763353714547913, 0.5137760333079378, 5.231694534023026, 8.7522634979224,
6.782498605460079, 4.11807952344139, 1.2977900867029324, 2.3997274593090556,
0.15838896234651623, 3.757328348884501, 0.5384642946347684, 1.1995761528111237,
1.4004601296516905, 3.224695090678109, 4.0767559979435575, 4.517724446184081,
6.8587291812564555, 2.8433778568332735, 8.34314308472577, 1.8803062830582484,
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5.285372338196844, 1.1778497669069798, 1.559562748897858, 3.6811765405561103,
0.6636163376087456, 9.374473666847713, 2.4112841981970567, 2.8381854361673264,
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5.081294282256998, 7.069862276305976, 4.772907835970401, 6.676727517173527,
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4.975208740425168, 8.661731830268737, 1.6964602183192867, 2.6692931146107646,
6.4338767780326815, 4.868275843700294, 5.559284856045961, 6.068822400277101,
4.77948482866273, 5.639648265365826, 0.10472243170756235, 5.948881449956518,
2.9549286836210653, 8.383000763138773, 6.2399090997234445, 4.660007510168757,
0.9996706653056076, 5.994272170067719, 9.246351546390024, 1.8966964723831725,
0.475840391126654, 8.54632535772652, 4.560884513302382, 5.387913714615083,
6.302598648702816, 9.958759058383801, 1.7813742791318932, 7.316117239291017,
2.813291724138469, 9.809682227983348, 7.708060380700297, 9.028271919774538,
9.47123012552268, 7.095565769183219, 7.36683153755493, 2.467582202007166,
6.864612386346513, 5.0949618589117}, {}, None}, "ElementType" -> "Real64"]],
TabularColumn[Association["Data" -> {{4.6966608878120155, 4.824312283883162,
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4.5503630336586625, 4.676774078885084, 0.2779261670640416, 4.725688518479375,
3.9127058431000403, 4.912771073391018, 4.706956433628692, 4.368940658754038,
1.9889028914188887, 4.757798616779952, 4.848641841535179, 3.011532716133896,
1.0434839585621511, 4.94165839642236, 4.470539925798977, 4.595770638864039,
4.719122945110612, 4.927458297680816, 2.954987997076547, 4.875258039312025,
3.832285369585581, 4.90716312590821, 4.886081978517763, 5.014719857724921,
4.995918853828975, 4.8432863841043785, 4.978770965631156, 3.565109756764496,
4.814056522876913, 4.662826256104416}, {}, None}, "ElementType" -> "Real64"]]}}]]]] -> "Voltage", ExponentialModel[]]DSolveValue[{v'[t] == (V - v[t]) / (R C), v[0] == 0}, v[t], t]//Simplify電圧ステップの後,インダクタを流れる電流は指数関数的に増加する:
ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Current" -> Association["ElementType" -> "Real64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
{{TabularColumn[Association["Data" -> {{2.212919562876545, 1.1561178651027557,
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4.0164328953783865, 3.1487457319666925, 5.967426177157303, 6.283330889447044,
6.559183506313567, 10.450766717688143, 5.388716469992724, 1.6407687545928509,
3.826826905357912, 4.020358188801509, 4.548356995042589, 1.0849943278070437,
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8.974161517773332, 2.6353694334312188, 4.4538041987787125, 7.035649949951655,
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3.836199061728779, 10.294469622826554, 5.146326790249952, 2.6572834034560007,
1.5110411278935683, 7.8585557765629055, 10.816602932120025, 11.514701763813106,
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1.3355721532621718, 5.044639825120428, 6.726474733164955, 10.148747709592602,
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4.115232724634687, 4.151777955416745, 9.911744741917094, 11.86938814669364,
4.582117504682287, 10.393489202778664}, {}, None}, "ElementType" -> "Real64"]],
TabularColumn[Association["Data" -> {{1.0665008742997244, 0.6386618092410143,
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1.861434900646372, 1.8576027502955512, 1.7356082879859034, 1.952183949113016,
1.8999864335540495, 1.9796378836122033, 1.6459722068534972, 0.09852052635097334,
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1.1738779508292967, 1.4732091065264568, 0.9768855265028001, 1.9367302180113446,
1.430449451894662, 1.8803286404996058, 1.6176480308898658, 1.2060017007782775,
0.7748884938391409, 1.889666631624872, 1.9681112294397822, 1.9756262311259676,
1.583234277859401, 1.2249936977408697, 1.9838519874468457, 1.946052038831788,
0.695508691588814, 1.6162550464295729, 1.812447513633879, 1.932406718536364,
1.7382107669962683, 1.807284572204545, 1.7343703250707732, 0.7269834339065654,
1.4111610222679576, 2.0188002854874196, 1.6652293961390294, 1.9521817654946807,
1.4915165887846673, 1.509758908636861, 1.9908931952073223, 1.9572957214845867,
1.593302883046879, 1.9105412685144856}, {}, None}, "ElementType" -> "Real64"]]}}]]]] -> "Current", ExponentialModel[]]物体の温度が周囲温度へ緩和する現象(ニュートンの冷却の法則):
ModelFit[Tabular[Association["RawSchema" -> Association["ColumnProperties" ->
Association["Time" -> Association["ElementType" -> "Real64"],
"Temperature" -> Association["ElementType" -> "Real64"]], "KeyColumns" -> None,
"Backend" -> "WolframKernel"], "Options" -> {},
"BackendData" -> Association["ColumnData" -> DataStructure["ColumnTable",
{{TabularColumn[Association["Data" -> {{1.285645967204907, 1.8712787246468885,
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6.025896132194127, 3.95526363138357}, {}, None}, "ElementType" -> "Real64"]],
TabularColumn[Association["Data" -> {{78.05020850105669, 70.8225882348392,
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1.9779949194867361}, {}, None}, "ElementType" -> "Real64"]]}}]]]] -> "Response", ExponentialModel[]]UnitStep入力に対する系の応答と比較する:
InputOutputResponse[TransferFunctionModel[{{{1}}, s + λ}, s], UnitStep[t], t]//First//Simplify人口増加 (1)
population = CountryData["World", {{"Population"}, {1950, 2000}}]model = ModelFit[population, ExponentialModel[]]predictions = TransformColumns[population, {"Prediction" -> (model[#Timestamp]&)}]ListPlot[{population -> {"Timestamp", "Value"}, predictions -> {"Timestamp", "Prediction"}}, ...]model[DateObject[{2020}]]CountryData["World", {"Population", 2020}]millenniumPopulation = CountryData["World", {{"Population"}, {2001, 2020}}]ListPlot[{millenniumPopulation -> {"Timestamp", "Value"}, TransformColumns[millenniumPopulation, {"Prediction" -> (model[#"Timestamp"]&)}] -> {"Timestamp", "Prediction"}}, ...]指数関数的である傾向がどこで停止したかを調査するために,1980年から2020年までの限定されたデータ範囲に対してフィットを行い,その損失を測定する:
allPopulation = Join[population, millenniumPopulation];損失は約31年前に劇的に増加し始めているが,これは人類の人口増加が1995年頃に指数関数的ではなくなったことを示している.
Table[{endDate, ModelFit[allPopulation[[ ;; endDate]], ExponentialModel[], "Report"]["Loss"]}, {endDate, Range[-40, -1]}]//ListLogPlot関連するガイド
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- 統計モデル解析
テキスト
Wolfram Research (2026), ExponentialModel, Wolfram言語関数, https://reference.wolfram.com/language/ref/ExponentialModel.html.
CMS
Wolfram Language. 2026. "ExponentialModel." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/ExponentialModel.html.
APA
Wolfram Language. (2026). ExponentialModel. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ExponentialModel.html
BibTeX
@misc{reference.wolfram_2026_exponentialmodel, author="Wolfram Research", title="{ExponentialModel}", year="2026", howpublished="\url{https://reference.wolfram.com/language/ref/ExponentialModel.html}", note=[Accessed: 07-September-2026]}
BibLaTeX
@online{reference.wolfram_2026_exponentialmodel, organization={Wolfram Research}, title={ExponentialModel}, year={2026}, url={https://reference.wolfram.com/language/ref/ExponentialModel.html}, note=[Accessed: 07-September-2026]}