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diff --git a/src/main/java/org/apache/commons/math3/ml/neuralnet/sofm/util/ExponentialDecayFunction.java b/src/main/java/org/apache/commons/math3/ml/neuralnet/sofm/util/ExponentialDecayFunction.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math3.ml.neuralnet.sofm.util;
+
+import org.apache.commons.math3.exception.NotStrictlyPositiveException;
+import org.apache.commons.math3.exception.NumberIsTooLargeException;
+import org.apache.commons.math3.util.FastMath;
+
+/**
+ * Exponential decay function: <code>a e<sup>-x / b</sup></code>,
+ * where {@code x} is the (integer) independent variable.
+ * <br/>
+ * Class is immutable.
+ *
+ * @since 3.3
+ */
+public class ExponentialDecayFunction {
+    /** Factor {@code a}. */
+    private final double a;
+    /** Factor {@code 1 / b}. */
+    private final double oneOverB;
+
+    /**
+     * Creates an instance. It will be such that
+     * <ul>
+     *  <li>{@code a = initValue}</li>
+     *  <li>{@code b = -numCall / ln(valueAtNumCall / initValue)}</li>
+     * </ul>
+     *
+     * @param initValue Initial value, i.e. {@link #value(long) value(0)}.
+     * @param valueAtNumCall Value of the function at {@code numCall}.
+     * @param numCall Argument for which the function returns
+     * {@code valueAtNumCall}.
+     * @throws NotStrictlyPositiveException if {@code initValue <= 0}.
+     * @throws NotStrictlyPositiveException if {@code valueAtNumCall <= 0}.
+     * @throws NumberIsTooLargeException if {@code valueAtNumCall >= initValue}.
+     * @throws NotStrictlyPositiveException if {@code numCall <= 0}.
+     */
+    public ExponentialDecayFunction(double initValue,
+                                    double valueAtNumCall,
+                                    long numCall) {
+        if (initValue <= 0) {
+            throw new NotStrictlyPositiveException(initValue);
+        }
+        if (valueAtNumCall <= 0) {
+            throw new NotStrictlyPositiveException(valueAtNumCall);
+        }
+        if (valueAtNumCall >= initValue) {
+            throw new NumberIsTooLargeException(valueAtNumCall, initValue, false);
+        }
+        if (numCall <= 0) {
+            throw new NotStrictlyPositiveException(numCall);
+        }
+
+        a = initValue;
+        oneOverB = -FastMath.log(valueAtNumCall / initValue) / numCall;
+    }
+
+    /**
+     * Computes <code>a e<sup>-numCall / b</sup></code>.
+     *
+     * @param numCall Current step of the training task.
+     * @return the value of the function at {@code numCall}.
+     */
+    public double value(long numCall) {
+        return a * FastMath.exp(-numCall * oneOverB);
+    }
+}